Immune system disorders characterize various diseases such as multiple sclerosis (MS) and allergic diseases (AD). In MS, T-helper (Th)1 cell phenotype is responsible for the disease onset and long-term evolution. On the other hand, excessive Th2 cell activity has been demonstrated in AD. The simultaneous increase of MS and AD in the same geographical areas, observed in recent years, has questioned the mutually exclusive correlation between MS and AD immunopathogenesis supported by the Th1/Th2 paradigm and has moved the interest in understanding possible overlaps. This manuscript aims to discuss the literature, collected over the past two decades, about the association between MS and AD, and both experimental and epidemiological studies have been reviewed. The results do not provide a solid correlation between AD and MS, although experimental studies support the involvement of the same cells and molecules in the immunopathogenesis of both diseases. Further studies, increasing knowledge on the cellular and molecular mechanisms involved in these two disorders, could help to clarify if a positive or negative association links them and provide the possibility for the development of new therapies.
Citation: Lisa Aielli, Federica Serra, Erica Costantini. Multiple sclerosis and allergic diseases: is there a relationship?[J]. AIMS Allergy and Immunology, 2022, 6(3): 126-152. doi: 10.3934/Allergy.2022011
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Immune system disorders characterize various diseases such as multiple sclerosis (MS) and allergic diseases (AD). In MS, T-helper (Th)1 cell phenotype is responsible for the disease onset and long-term evolution. On the other hand, excessive Th2 cell activity has been demonstrated in AD. The simultaneous increase of MS and AD in the same geographical areas, observed in recent years, has questioned the mutually exclusive correlation between MS and AD immunopathogenesis supported by the Th1/Th2 paradigm and has moved the interest in understanding possible overlaps. This manuscript aims to discuss the literature, collected over the past two decades, about the association between MS and AD, and both experimental and epidemiological studies have been reviewed. The results do not provide a solid correlation between AD and MS, although experimental studies support the involvement of the same cells and molecules in the immunopathogenesis of both diseases. Further studies, increasing knowledge on the cellular and molecular mechanisms involved in these two disorders, could help to clarify if a positive or negative association links them and provide the possibility for the development of new therapies.
Disease is one of the greatest threats to human health. Many people die due to various diseases each year. With the development of medical science, several efforts have been given to various diseases [1,2,3], with an ultimate purpose of uncovering the underlying mechanism and the essential characteristics of different diseases to design efficient treatments. To date, several treatments have been designed to deal with different diseases, and drug is deemed as one of the most important parts. For fast indication of the utility of drugs, they are divided into several classes. Drugs in one class always have some common features. Correctly identifying the class of a candidate drug-like compound is helpful to uncover its effects. The classification procedure could be completed by traditional experiments. However, such methods are always inefficient and expensive. Designing quick and cheap methods that could accurately predict the class of a given drug is urgent. With the development of computer science, many advanced computational methods have been proposed in recent years and applied to deal with various practical problems, such as artificial neural networks [4,5,6], feature selection algorithms [7], statistical test methods [8,9,10]. These methods provide strong support for designing efficient methods.
Designing computational methods to classify drugs have recently become relatively popular. The most representative studies focused on predicting drug classes in the Anatomical Therapeutic Chemical (ATC) classification system. Such system is recommended and maintained by the World Health Organization (WHO). It has five levels, and several classes are contained in each level. Many computational methods have been proposed to predict classes in the first level of the ATC classification system. Most of them were based on machine learning algorithms, such as deep learning algorithms [11,12,13,14], network embedding algorithms [15], and multi-label classification algorithms [15,16,17]. Meanwhile, several types of drug properties were adopted to build methods that include drug fingerprints [16,17] and chemical–chemical similarity/interaction information [13,18,19,20,21]. These methods provide helpful insights into investigating other drug classification systems.
The Kyoto Encyclopedia of Genes and Genomes (KEGG) [22] recently reported a new drug classification system that classified drugs into the following 14 classes: (Ⅰ) anti-allergic agent; (Ⅱ) anti-inflammatory; (Ⅲ) antibacterial; (Ⅳ) antidiabetic agent; (Ⅴ) antifungal; (Ⅵ) antineoplastic; (Ⅶ) antiviral; (Ⅷ) cardiovascular agent; (Ⅸ) endocrine and hormonal agent; (Ⅹ) gastrointestinal agent; (Ⅺ) hypolipidemic agent; (Ⅻ) immunological agent; (XIII) neuropsychiatric agent; (XIV) ophthalmic agent. Under this system, drugs are roughly classified in accordance with the diseases they could treat. Identifying the class of any latent drug-like compound is helpful to preliminarily determine its effects to a certain type of disease. Targeted experiments for this type of disease could subsequently be conducted for further confirmation, which could accelerate the procedures for discovering novel drugs. Thus, for the first time, such a drug classification system was investigated in this study by developing a classification model, which could assign one of the above 14 classes to given drugs. A large drug network was constructed on the basis of chemical–chemical interaction (CCI) information reported in Search Tool for Interactions of Chemicals (STITCH) to extract informative drug features [23,24]. It was fed into a powerful network embedding algorithm called Node2vec [25] to produce drug features. These features were quite different from traditional drug fingerprint features, which could be obtained by considering each drug individually. The features adopted in this study abstracted drug associations, and they could overview each drug with all drugs as background, thus representing drugs at a system level. They were learnt by support vector machine (SVM) [26] or random forest (RF) [27] to construct the model. The cross-validation results indicated the good performance of the model. The model was also superior to those with classic drug fingerprint features or embedding features yielded by another network embedding algorithm. The proposed model adopted synthetic minority oversampling technique (SMOTE) [28] to tackle the imbalance problem, and the results suggested that it could balance the performance across different classes.
Drugs and their information were retrieved from KEGG DRUG (https://www.genome.jp/kegg/drug/) [22]. According to https://www.kegg.jp/brite/br08332 (accessed on 10 January 2022), 820 drugs encoded by KEGG IDs were classified into 14 classes, as mentioned in Section 1 and listed in column 2 of Table 1. For convenience, these classes were tagged as C1–C14. The correspondence between tags and classes could be found in columns 1 and 2 of Table 1. Among these drugs, only two belong to two classes, while the others are in one exact class. Thus, these two drugs were removed. As the CCI information was employed to build the model, where drugs are represented by PubChem IDs, the KEGG IDs with unavailable PubChem IDs were also removed. If the PubChem ID of one KEGG ID was not included in the drug network (Section 2.2), it was also discarded. Finally, 579 drugs encoded by KEGG IDs were obtained. These drugs were classified into 14 classes, as listed in column 2 of Table 1. The number of drugs in each class is also listed in Table 1. The detailed drugs in each class are provided in S1.
| Tag | Drug class | Number of drugs |
| C1 | Anti-allergic agent | 35 |
| C2 | Anti-inflammatory | 116 |
| C3 | Antibacterial | 99 |
| C4 | Antidiabetic agent | 25 |
| C5 | Antifungal | 21 |
| C6 | Antineoplastic | 51 |
| C7 | Antiviral | 44 |
| C8 | Cardiovascular agent | 54 |
| C9 | Endocrine and hormonal agent | 19 |
| C10 | Gastrointestinal agent | 29 |
| C11 | Hypolipidemic agent | 13 |
| C12 | Immunological agent | 8 |
| C13 | Neuropsychiatric agent | 37 |
| C14 | Ophthalmic agent | 28 |
| Total | 579 | |
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Recently, network is a popular form to investigate various complex biological and medical problems [15,29,30,31], because it could overview all objects (nodes in the network) and evaluate them at a system level. In the present study, network was utilized to organize drugs.
The constructed network defined drugs reported in KEGG as nodes. The relationship between nodes should be determined to form the network. Several public databases provide the existing associations of drugs. Here, the CCI information reported in STITCH (http://stitch.embl.de/, version 4.0) was employed [23,24]. The file named "chemical_chemical.links.v4.0.tsv.gz" was downloaded from STITCH, which contains a large number of CCIs. Each CCI consists of two chemicals, represented by PubChem IDs, and one confidence score. The confidence score is obtained by measuring several aspects of chemicals, such as structures, reactions, activities, and literature descriptions. It ranges between 1 and 999 with a meaning that the higher the confidence score, the stronger the association between two chemicals. For formulation, the confidence score on the CCI between chemicals c1 and c2 was denoted as S(c1, c2). For the constructed network, two nodes were connected by an edge if their corresponding drugs could comprise a CCI with a confidence score larger than zero. Evidently, each edge in the network indicated a CCI. Each edge was assigned a weight, which was defined as S(c1, c2)/1000, to reflect different strengths of CCIs. Such network was denoted by Nd, and it contained 17,956 nodes and 3,134,797 edges.
Encoding samples into continuous vectors that could be processed by most computer algorithms is one of the most important steps to build efficient classification models. As mentioned in Section 2.2, a drug network was constructed, which contained informative associations of drugs. This information could be used to encode drugs. In recent years, several network embedding algorithms, such as DeepWalk [32], Mashup [33], and LINE [34], have been proposed to assign continuous vectors to nodes in one or more networks. Here, Node2vec [25] was selected to extract drug features from Nd.
Node2vec is a powerful network embedding algorithm. In fact, it could be deemed as an improved version of DeepWalk. Similar to DeepWalk, it samples many paths from a given network. The procedures of generating paths are greatly improved compared with those in DeepWalk. For each node u, Node2vec produces a predefined number of paths starting at u. Suppose that ni-1 is the (i-1)-th node in one path starting at u. This path is extended to the i-th node, denoted by ni, by selecting one neighbor of ni-1. The selection probability from ni-1 to any other node is defined as
| P(ni=w|ni−1=v)={πvw/Zifw is adjacent to v0Otherwise, | (1) |
where πvw stands for the transition probability from v to w, defined by πvw=αpq(t,w)⋅wvw; Z is the sum of transition probabilities from v to all its neighbors; and wvw denotes the weight on edge connecting nodes v and w. Moreover, αpq(t,w) could be determined by
| αpq(t,w)={1/pif dtw=01if dtw=11/qif dtw=2, | (2) |
where t is the (i-2)-th node in the path, and dtw represents the distance between t and w. For the path starting at u, it is iteratively extended by selecting a neighbor of the current end-point in accordance with the probabilities calculated using Eq (1) until the length of the path reaches the predefined length. After all paths are produced, they are fed into the word2vec algorithm with SkipGram to produce vectors of nodes, where the paths are deemed as sentences and nodes are considered as words.
In this study, the program of Node2vec was retrieved from https://snap.stanford.edu/node2vec/. Such program was performed on Nd by using its default parameters. Afterwards, the feature vectors of 579 drugs were picked up to construct models.
Besides the representation of samples, another key step to build efficient classification models is to select a proper classification algorithm. In this study, two classic classification algorithms were used: SVM [26] and RF [27]. These two algorithms have been widely used to set up classification models with excellent performance for investigating various biological [35,36,37,38,39] and medical problems [40,41,42,43].
SVM is a statistical learning-based classification algorithm. Its original version could only process binary classification problems. Its idea is to determine an optimal hyperplane that could separate samples into two classes as perfect as possible. However, such hyperplane is not easy to obtain in the original feature subspace. It generally employs kernel trick to map samples into a high-dimension space, in which the hyperplane, designed to separate samples in two classes, could be easily constructed. A test sample is also mapped into the high-dimension space. The class of the test sample is determined in accordance with the side of the hyperplane it is located. Subsequently, SVM could tackle multi-class classification problems by using "one-versus-rest" or "one-versus-one".
RF is another powerful and widely used classification algorithm, which is quite different from SVM. In fact, it is a type of ensemble algorithm. Several decision trees are constructed and comprise RF. Some features are randomly selected from all features to learn a decision tree. Samples are randomly selected, with replacement, from the original dataset. Then, a decision tree is built on the basis of these randomly selected features and samples. RF gives the prediction by majority voting on predictions generated by the decision trees it contains.
In this study, the tools "SMO" and "RandomForest" in Weka were directly used [44], and they implemented the above mentioned SVM and RF, respectively. These tools were used with their default parameters.
A total of 14 drug classes were involved in this study. Some classes contained much less samples than the others, implying the dataset was imbalanced. The classification model directly built on such dataset may provide excellent performance on the majority class and poor performance on the minority class. SMOTE [28] was employed to tackle such problem. For each class, except the largest, one sample is randomly selected, denoted by x. The k nearest neighbors of x in the same class are found, and one is randomly selected, say y. On the basis of x and y, a new sample z is generated, which is defined as the linear combination of x and y with randomly produced combination coefficients. Given that the new sample is highly related to x and y, it is placed into the same class of x and y. The above procedures are executed several times until the size of the minority class is the same as that of the largest class.
This study adopted the tool "SMOTE" in Weka [44] to balance the investigated dataset, and default parameters were used.
As a multi-class classification problem, the performance of the models could be measured by overall accuracy (ACC), which is defined as the proportion of all correctly predicted samples.
Recall, precision, F-measure, and Matthew's correlation coefficient (MCC) [45] were also computed for each class as follows:
| Precision=TPTP+FP, | (3) |
| Recall=TPTP+FN, | (4) |
| F−measure=2×Precision×RecallPrecision+Recall, | (5) |
| MCC=TP×TN−FP×FN√(TN+FN)×(TN+FP)×(TP+FN)×(TP+FP), | (6) |
where TP, FP, FN, and TN stand for true positive, false positive, false negative, and true negative, respectively. In multi-class classification, their specific definitions for one class are as follows: TP is the number of samples in the class that are also classified into this class, FP is the number of samples not in the class that are classified into this class, FN is the number of samples in the class that are classified into other classes, and TN is the number of samples not in the class that are classified into other classes. Eqs (3)–(6) could only assess the performance under a certain threshold. For full assessment, the receiver operating characteristic (ROC) and precision-recall (PR) curve analyses were further employed on the predicted results for each class. The area under the ROC curve, which was denoted by AUROC, and the PR curve, which was represented by AUPR, were calculated to fully evaluate the performance of different models on each class. For easy description, the six measurements for class Ci were denoted by precision(i), recall(i), F-measure(i), MCC(i), AUROC(i), and AUPR(i). Furthermore, the weighted precision, recall, F-measure, MCC, AUROC, and AUPR were calculated to fully evaluate the overall performance of all classification models.
In this study, a novel model was proposed to classify drugs in accordance with the drug classification system recently reported in KEGG. Its construction and evaluation procedures are illustrated in Figure 1. In this section, detailed evaluation results were provided, and further comparisons were conducted.
The proposed model adopted the drug features derived from a large drug network via Node2vec and SMOTE to tackle the imbalance problem. The best dimension of the features was found to be 128. Two classic classification algorithms (RF and SVM) were used as the prediction engine. For easy description, if RF (SVM) was selected as the prediction engine, the model was called RF (SVM) model. Each model was evaluated five times using a 10-fold cross-validation [46]. The confusion matrix of each model under each 10-fold cross-validation is provided in S2. The average performance is listed in Table 2.
| Classification algorithm | SMOTE | ACC | Weighted Precision | Weighted F-measure | Weighted MCC | Weighted AUROC | Weighted AUPR |
| Random forest | √ | 0.9486 | 0.9631 | 0.9511 | 0.9484 | 0.9958 | 0.9830 |
| Support vector machine | √ | 0.9583 | 0.9752 | 0.9640 | 0.9625 | 0.9830 | 0.9350 |
| Random forest | ⊆ | 0.9410 | - | - | - | 0.9930 | 0.9710 |
| Support vector machine | ⊆ | 0.9600 | 0.9610 | 0.9600 | 0.9550 | 0.9840 | 0.9320 |
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For the RF model, the ACC was 0.9486, and the weighted precision, F-measure, MCC, AUROC, and AUPR were 0.9631, 0.9511, 0.9484, 0.9958, and 0.9830, respectively. The result of each measurement was relatively high, suggesting the excellent performance of the RF model. The model's performance on 14 classes is provided in Table 3, and the ROC and PR curves under one 10-fold cross-validation are shown in Figure 2(A), (B), respectively. Table 3 shows that most measurements were higher than 0.9. The number of classes on which the recall values were higher than 0.9 was 12, and this numbers for precision, F-measure, MCC, AUPRO, and AUPR were 11, 12, 12, 14, and 13, respectively. The results indicated the good performance of the RF model on most classes, conforming to its overall performance. Careful examination of the measurements in Table 3 showed that the RF model provided the lowest performance on class C12 (immunological agent) for each measurement. For example, the recall on this class was only 0.2726. This class only contained eight drugs, which was not only the smallest among all 14 classes, but was considered the lowest performing class. Although the RF model adopted SMOTE to tackle the imbalance problem, the class sizes could still have influence. In the following text, we would elaborate that such influence has been decreased by SMOTE.
| Tag of class | Recall(i) | Precision(i) | F-measure(i) | MCC(i) | AUROC(i) | AUPR(i) |
| C1 | 0.9052 | 0.9319 | 0.9141 | 0.9162 | 0.9975 | 0.9627 |
| C2 | 0.9550 | 0.9449 | 0.9469 | 0.9366 | 0.9972 | 0.9879 |
| C3 | 0.9983 | 0.9761 | 0.9865 | 0.9845 | 0.9998 | 0.9991 |
| C4 | 0.9750 | 0.9749 | 0.9690 | 0.9708 | 0.9994 | 0.9921 |
| C5 | 0.9676 | 1.0000 | 0.9804 | 0.9812 | 0.9943 | 0.9780 |
| C6 | 0.9473 | 0.9113 | 0.9179 | 0.9182 | 0.9915 | 0.9770 |
| C7 | 0.9434 | 0.9611 | 0.9450 | 0.9453 | 0.9932 | 0.9769 |
| C8 | 0.9377 | 0.9829 | 0.9548 | 0.9537 | 0.9940 | 0.9709 |
| C9 | 0.9191 | 0.9294 | 0.9499 | 0.9511 | 0.9972 | 0.9659 |
| C10 | 0.9132 | 0.8882 | 0.8858 | 0.8886 | 0.9969 | 0.9602 |
| C11 | 0.8870 | 0.8528 | 0.9582 | 0.9622 | 1.0000 | 1.0000 |
| C12 | 0.2726 | 0.5183 | 0.5834 | 0.5852 | 0.9127 | 0.7654 |
| C13 | 0.9781 | 0.9456 | 0.9552 | 0.9557 | 0.9997 | 0.9958 |
| C14 | 0.9831 | 0.9738 | 0.9768 | 0.9764 | 0.9998 | 0.9967 |
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As for the SVM model, the ACC reached 0.9583, which was slightly higher than that of the RF model. As for the other five measurements, they were 0.9752, 0.9640, 0.9625, 0.9830 and 0.9350 (Table 2), respectively. Compared with the measurements of RF model, SVM model yielded higher weighted precision, F-measure, MCC, whereas lower weighted AUROC and AUPR. These indicated that the RF and SVM model provided almost equal performance. The detailed performance of SVM model on fourteen classes is listed in Table 4. Furthermore, the ROC and PR curves of this model under one 10-fold cross-validation are illustrated in Figure 2(C), (D), respectively. Similar to the performance of RF model, most measurements listed in this table were higher than 0.9. The numbers of classes on which recall, precision, F-measure, MCC, AUROC and AUPR, respectively, were higher than 0.9 were 12, 13, 13, 13, 14 and 11, which were also similar to those of the RF model. Again, the performance of SVM model on the smallest class C12 (immunological agent) was also lowest for each measurement.
| Tag of class | Recall(i) | Precision(i) | F-measure(i) | MCC(i) | AUROC(i) | AUPR(i) |
| C1 | 0.9444 | 0.9152 | 0.9211 | 0.9215 | 0.9934 | 0.8984 |
| C2 | 0.9635 | 0.9584 | 0.9593 | 0.9513 | 0.9882 | 0.9428 |
| C3 | 0.9967 | 0.9803 | 0.9877 | 0.9860 | 0.9982 | 0.9803 |
| C4 | 0.9717 | 1.0000 | 0.9831 | 0.9838 | 0.9982 | 0.9836 |
| C5 | 0.9676 | 0.9867 | 0.9704 | 0.9725 | 0.9916 | 0.9603 |
| C6 | 0.9556 | 0.9656 | 0.9549 | 0.9540 | 0.9857 | 0.9604 |
| C7 | 0.9384 | 0.9598 | 0.9407 | 0.9412 | 0.9700 | 0.9197 |
| C8 | 0.9569 | 0.9805 | 0.9650 | 0.9637 | 0.9905 | 0.9546 |
| C9 | 0.9109 | 1.0000 | 0.9756 | 0.9759 | 0.9964 | 0.9644 |
| C10 | 0.8567 | 0.9282 | 0.9049 | 0.9096 | 0.9623 | 0.8393 |
| C11 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| C12 | 0.7373 | 0.4740 | 0.6904 | 0.7134 | 0.9270 | 0.5556 |
| C13 | 0.9790 | 0.9843 | 0.9786 | 0.9788 | 0.9993 | 0.9814 |
| C14 | 0.9841 | 0.9944 | 0.9874 | 0.9875 | 0.9985 | 0.9874 |
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According to Table 2, the SVM model provided higher performance on four measurements than the RF model, whereas the RF model yielded higher performance on the other two measurements. The SVM model was slightly superior to the RF model. Several box plots were drawn on all measurements for the performance of the two models on 14 classes, as shown in Figure 3. For recall, the SVM model varied in a smaller range than the RF model. The same case occurred for F-measure, MCC, and AUROC. As for the remaining two measurements (precision and AUPR), the RF model changed in a smaller interval. On the whole, the stability of the performance of these two models on different classes was at the same level. However, the SVM model was slightly more stable. These results showed that the SVM model is a more suitable tool for classifying drugs than the RF model.
In this study, SMOTE was adopted to tackle the imbalance problem. The performance of the model without SMOTE must be investigated and compared with that of the model with SMOTE. In view of this, RF and SVM were directly applied on the features yielded by Node2vec to build the RF and SVM models without SMOTE. Their performance was also evaluated five times using a 10-fold cross-validation. The average performance is listed in Table 2. For the RF model without SMOTE, the ACC, weighted AUROC, and AUPR were 0.9410, 0.9930, and 0.9710, respectively. As no drugs were classified into C12, the precision, F-measure, and MCC for this class could not be computed, further inducing no results for weighted precision, F-measure, and MCC. According to the computed measurements, the performance of this model was only slightly lower than that of the RF model with SMOTE. As for the SVM model without SMOTE, the six measurements were 0.9600, 0.9610, 0.9600, 0.9550, 0.9840, and 0.9320, which were relatively similar to those of the SVM model with SMOTE. This finding suggested that the employment of SMOTE did not improve the overall performance of the model at all. However, the utilization of SMOTE was reflected not only on the improvement of the overall performance of the models but also on the balance of performance across different classes. Thus, the accuracy, i.e., recall, on the 14 classes yielded by the models with or without SMOTE was further investigated. A box plot was drawn for each model to clearly show the change in recall on all classes, as shown in Figure 4. When the same prediction engine was used (RF or SVM), the recall values of the model with SMOTE varied in a smaller interval than those of the model without SMOTE. This result implied that the employment of SMOTE balanced the performance across different classes. For example, on C12, the RF and SVM models without SMOTE produced recall values of 0.0000 and 0.5000, respectively, and these values were improved to 0.2726 and 0.7373, respectively, by employing SMOTE. For practical applications, the model with SMOTE could avoid the extremely good performance on the majority class and extremely low performance on the minority class.
In this study, the drug features derived from a network via Node2vec were used to build the model. Two other feature types were adopted to build models, which were compared with the proposed model.
The first feature type contained features derived from the drug network Nd via another powerful network embedding algorithm, Mashup [33]. The dimension was also set to 128. RF and SVM were applied on these features to construct models. SMOTE was employed to tackle the imbalance problem. The models were also assessed five times using a 10-fold cross-validation. For clear description, they were called Mashup-based RF and SVM models, whereas the proposed models were termed as Node2vec-based RF and SVM models. The average performance of the Mashup-based models is provided in Table 5. When RF was selected as the prediction engine, the Node2vec-based model provided evidently higher performance on all measurements than the Mashup-based model. In detail, the improvement on ACC, weighted F-measure, MCC, and AUPR was about 0.05 and such improvement on weighted precision and AUROC was about 0.03 and 0.01, respectively. Meanwhile, the superiority of the Node2vec-based SVM model to the Mashup-based SVM model was not very evident. However, the Node2vec-based SVM model provided higher performance on five measurements, implied that it was slightly better than the Mashup-based model.
| Drug features | Classification algorithm | ACC | Weighted Precision | Weighted F-measure | Weighted MCC | Weighted AUROC | Weighted AUPR |
| Drug features produced by Mashup | Random forest | 0.8891 | 0.9282 | 0.9069 | 0.9006 | 0.9819 | 0.9359 |
| Support vector machine | 0.9526 | 0.9724 | 0.9617 | 0.9579 | 0.9880 | 0.9280 | |
| Drug fingerprint features | Random forest | 0.8432 | 0.8768 | 0.8431 | 0.8366 | 0.9748 | 0.9107 |
| Support vector machine | 0.8426 | 0.8791 | 0.8489 | 0.8420 | 0.9476 | 0.7943 |
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The second feature type included the drug fingerprint features, which were widely used to deal with various drug- or chemical-related problems [16,38,47,48,49]. Here, RDKit [50] was adopted to extract the ECFP fingerprints of all 579 drugs. The fingerprints of each drug were represented by a binary vector with dimension of 1024. These fingerprint features were fed into RF and SVM to construct models. SMOTE was also employed. The models were called fingerprint-based models. All models were evaluated five times using a 10-fold cross-validation. The average performance is provided in Table 5. The results showed that most measurements were lower than 0.9, indicating low performance of such models. Compared with the performance of the proposed model (Table 2), the proposed model clearly provided much higher performance.
Finally, the ROC and PR curves on each class for some models were plotted. For the Node2vec-based model, the SVM model was selected because it was slightly better and more stable than the RF model. For the Mashup-based model, the SVM model was chosen as it was superior to the RF model (Table 5). As for the fingerprint-based model, the RF model was selected due to its superiority to the SVM model (Table 5). The ROC and PR curves of these models on all classes are shown in Figure 5. A notable detail that these curves were produced by one of the five 10-fold cross-validations. The areas under the ROC curves of the fingerprint-based RF model were much smaller than those of other two models. The same results were obtained for PR curves. As for the Node2vec-based and Mashup-based SVM models, the areas under the ROC or PR curves were relatively similar. However, the areas under the ROC or PR curves of the Mashup-based SVM model on some classes (e.g., C12) were relatively low, whereas they were improved by the Node2vec-based SVM model.
Therefore, the proposed model (Node2vec-based model) was better than the models using other drug features, indicating its superiority in classifying drugs.
In Section 3.3, the proposed model and two other models with different drug features were compared. The proposed model provided better performance than the two other models, suggesting that the features used in the proposed model were more efficient than those adopted in other models for drug classification. The t-SNE [51] was adopted to analyze three feature types used in the Node2vec-based, Mashup-based, and fingerprint-based models to further confirm these findings and provide intuitive evidence. Such tool could reduce the dimensions of features to two and display them in a 2D space, where samples in different classes are in different colors. The results of t-SNE are illustrated in Figure 6. The features derived from the drug network [Figure 6(A),(B)] could cluster drugs in different classes evidently better than the drug fingerprints [Figure 6(C)]. As for two feature types derived from the drug network, the features yielded by Node2vec [Figure 6(A)] could cluster drugs in different classes more compactly than those generated by Mashup. These results implied that the features derived from the drug network via Node2vec, which were used in the proposed model, were more helpful in classifying drugs. This finding was the main reason why the Node2vec-based model could provide better performance.
In this study, a new drug classification system reported in KEGG was investigated by proposing classification models for assigning class in this system to any given drug. Informative drug features were derived from a large drug network via a powerful network embedding algorithm. The evaluation and comparison results indicated that the features were more related to the drug classification system in KEGG and more informative than classic fingerprint features. Employing more drug information (e.g., drug side effects and indications) or more complex and advanced feature learning algorithms (e.g., graph convolutional network and convolutional neural network) may be helpful to access more informative drug features, thereby building more powerful classification models. These aspects could be investigated in future work. Furthermore, the employment of SMOTE guaranteed that the performance on all classes was high. The model could be a useful tool to classify drugs, discover novel effects of existing drugs and determine the effects of candidate drug-like compounds.
The authors declare there is no conflict of interest.
| [1] |
Akdis M, Akdis CA (2014) Mechanisms of allergen-specific immunotherapy: multiple suppressor factors at work in immune tolerance to allergens. J Allergy Clin Immunol 133: 621-631. https://doi.org/10.1016/j.jaci.2013.12.1088 
|
| [2] |
Buckley CD, McGettrick HM (2018) Leukocyte trafficking between stromal compartments: lessons from rheumatoid arthritis. Nat Rev Rheumatol 14: 476-487. https://doi.org/10.1038/s41584-018-0042-4
|
| [3] | Kidd P (2003) Th1/Th2 balance: the hypothesis, its limitations, and implications for health and disease. Altern Med Rev 8: 223-246. |
| [4] |
Romagnani S (2004) Immunologic influences on allergy and the TH1/TH2 balance. J Allergy Clin Immunol 113: 395-400. https://doi.org/10.1016/j.jaci.2003.11.025
|
| [5] |
Lee GR, Kim ST, Spilianakis CG, et al. (2006) T helper cell differentiation: regulation by cis elements and epigenetics. Immunity 24: 369-379. https://doi.org/10.1016/j.immuni.2006.03.007
|
| [6] |
Matsuzaki J, Tsuji T, Imazeki I, et al. (2005) Immunosteroid as a regulator for Th1/Th2 balance: its possible role in autoimmune diseases. Autoimmunity 38: 369-375. https://doi.org/10.1080/08916930500124122
|
| [7] |
Ngoc PL, Gold DR, Tzianabos AO, et al. (2005) Cytokines, allergy, and asthma. Curr Opin Allergy Clin Immunol 5: 161-166. https://doi.org/10.1097/01.all.0000162309.97480.45
|
| [8] |
Podbielska M, Banik NL, Kurowska E, et al. (2013) Myelin recovery in multiple sclerosis: the challenge of remyelination. Brain Sci 3: 1282-1324. https://doi.org/10.3390/brainsci3031282
|
| [9] |
Arellano G, Acuña E, Reyes LI, et al. (2017) Th1 and Th17 cells and associated cytokines discriminate among clinically isolated syndrome and multiple sclerosis phenotypes. Front Immunol 8: 753. https://doi.org/10.3389/fimmu.2017.00753
|
| [10] |
Wang K, Song F, Fernandez-Escobar A, et al. (2018) The properties of cytokines in multiple sclerosis: pros and cons. Am J Med Sci 356: 552-560. https://doi.org/10.1016/j.amjms.2018.08.018
|
| [11] |
Hirahara K, Nakayama T (2016) CD4+ T-cell subsets in inflammatory diseases: beyond the Th1/Th2 paradigm. Int Immunol 28: 163-171. https://doi.org/10.1093/intimm/dxw006
|
| [12] |
Wambre E, Bajzik V, DeLong JH, et al. (2017) A phenotypically and functionally distinct human TH2 cell subpopulation is associated with allergic disorders. Sci Transl Med 9: eaam9171. https://doi.org/10.1126/scitranslmed.aam9171
|
| [13] |
Tang C, Inman MD, van Rooijen N, et al. (2001) Th type 1-stimulating activity of lung macrophages inhibits Th2-mediated allergic airway inflammation by an IFN-gamma-dependent mechanism. J Immunol 166: 1471-1481. https://doi.org/10.4049/jimmunol.166.3.1471
|
| [14] |
Tang L, Benjaponpitak S, DeKruyff RH, et al. (1998) Reduced prevalence of allergic disease in patients with multiple sclerosis is associated with enhanced IL-12 production. J Allergy Clin Immunol 102: 428-435. https://doi.org/10.1016/S0091-6749(98)70131-9
|
| [15] |
Okada H, Kuhn C, Feillet H, et al. (2010) The “hygiene hypothesis” for autoimmune and allergic diseases: an update. Clin Exp Immunol 160: 1-9. https://doi.org/10.1111/j.1365-2249.2010.04139.x
|
| [16] |
Bach JF (2002) The effect of infections on susceptibility to autoimmune and allergic diseases. N Engl J Med 347: 911-920. https://doi.org/10.1056/NEJMra020100
|
| [17] |
Cunniffe N, Coles A (2021) Promoting remyelination in multiple sclerosis. J Neurol 268: 30-44. https://doi.org/10.1007/s00415-019-09421-x
|
| [18] | Lazibat I, Rubinić-Majdak M, Županić S (2018) Multiple sclerosis: new aspects of immunopathogenesis. Acta Clin Croat 57: 352-361. https://doi.org/10.20471/acc.2018.57.02.17 |
| [19] |
Yamout B, Alroughani R (2018) Multiple sclerosis. Semin Neurol 38: 212-225. https://doi.org/10.1055/s-0038-1649502
|
| [20] |
Dobson R, Giovannoni G (2019) Multiple sclerosis—a review. Eur J Neurol 26: 27-40. https://doi.org/10.1111/ene.13819
|
| [21] |
Walton C, King R, Rechtman L, et al. (2020) Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult Scler 26: 1816-1821. https://doi.org/10.1177/1352458520970841
|
| [22] |
Filippi M, Bar-Or A, Piehl F, et al. (2018) Multiple sclerosis. Nat Rev Dis Primers 4: 43. https://doi.org/10.1038/s41572-018-0041-4
|
| [23] |
Lassmann H, Brück W, Lucchinetti CF (2007) The immunopathology of multiple sclerosis: an overview. Brain Pathol 17: 210-218. https://doi.org/10.1111/j.1750-3639.2007.00064.x
|
| [24] |
Kearney H, Altmann DR, Samson RS, et al. (2015) Cervical cord lesion load is associated with disability independently from atrophy in MS. Neurology 84: 367-373. https://doi.org/10.1212/WNL.0000000000001186
|
| [25] |
Schiess N, Calabresi PA (2016) Multiple sclerosis. Semin Neurol 36: 350-356. https://doi.org/10.1055/s-0036-1585456
|
| [26] |
Dendrou CA, Fugger L, Friese MA (2015) Immunopathology of multiple sclerosis. Nat Rev Immunol 15: 545-558. https://doi.org/10.1038/nri3871
|
| [27] |
Amato MP, Ponziani G, Siracusa G, et al. (2001) Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol-Chicago 58: 1602-1606. https://doi.org/10.1001/archneur.58.10.1602
|
| [28] |
Oh J, Vidal-Jordana A, Montalban X (2018) Multiple sclerosis: clinical aspects. Curr Opin Neurol 31: 752-759. https://doi.org/10.1097/WCO.0000000000000622
|
| [29] |
McGinley MP, Goldschmidt CH, Rae-Grant AD (2021) Diagnosis and treatment of multiple sclerosis: a review. JAMA 325: 765-779. https://doi.org/10.1001/jama.2020.26858
|
| [30] |
Przybek J, Gniatkowska I, Mirowska-Guzel D, et al. (2015) Evolution of diagnostic criteria for multiple sclerosis. Neurol Neurochir Pol 49: 313-321. https://doi.org/10.1016/j.pjnns.2015.07.006
|
| [31] |
Milo R, Miller A (2014) Revised diagnostic criteria of multiple sclerosis. Autoimmun Rev 13: 518-524. https://doi.org/10.1016/j.autrev.2014.01.012
|
| [32] |
Garg N, Smith TW (2015) An update on immunopathogenesis, diagnosis, and treatment of multiple sclerosis. Brain Behav 5: e00362. https://doi.org/10.1002/brb3.362
|
| [33] |
Klineova S, Lublin FD (2018) Clinical course of multiple sclerosis. Cold Spring Harb Perspect Med 8: a028928. https://doi.org/10.1101/cshperspect.a028928
|
| [34] | Hart FM, Bainbridge J (2016) Current and emerging treatment of multiple sclerosis. Am J Manag Care 22: s159-s170. |
| [35] |
Baecher-Allan C, Kaskow BJ, Weiner HL (2018) Multiple sclerosis: mechanisms and immunotherapy. Neuron 97: 742-768. https://doi.org/10.1016/j.neuron.2018.01.021
|
| [36] |
Alfredsson L, Olsson T (2019) Lifestyle and environmental factors in multiple sclerosis. Cold Spring Harb Perspect Med 9: a028944. https://doi.org/10.1101/cshperspect.a028944
|
| [37] | Selter RC, Hemmer B (2013) Update on immunopathogenesis and immunotherapy in multiple sclerosis. ImmunoTargets Ther 2: 21-30. https://doi.org/10.2147/ITT.S31813 |
| [38] |
Baxter AG (2007) The origin and application of experimental autoimmune encephalomyelitis. Nat Rev Immunol 7: 904-912. https://doi.org/10.1038/nri2190
|
| [39] |
Kuerten S, Lichtenegger FS, Faas S, et al. (2006) MBP-PLP fusion protein-induced EAE in C57BL/6 mice. J Neuroimmunol 177: 99-111. https://doi.org/10.1016/j.jneuroim.2006.03.021
|
| [40] |
Derdelinckx J, Cras P, Berneman ZN, et al. (2021) Antigen-specific treatment modalities in MS: The past, the present, and the future. Front Immunol 12: 624685. https://doi.org/10.3389/fimmu.2021.624685
|
| [41] |
D'Angelo C, Reale M, Costantini E, et al. (2018) Profiling of canonical and non-traditional cytokine levels in interferon-β-treated relapsing-remitting-multiple sclerosis patients. Front Immunol 9: 1240. https://doi.org/10.3389/fimmu.2018.01240
|
| [42] |
Marchetti L, Engelhardt B (2020) Immune cell trafficking across the blood-brain barrier in the absence and presence of neuroinflammation. Vasc Biol 2: H1-H18. https://doi.org/10.1530/VB-19-0033
|
| [43] |
Gold R, Wolinsky JS (2011) Pathophysiology of multiple sclerosis and the place of teriflunomide. Acta Neurol Scand 124: 75-84. https://doi.org/10.1111/j.1600-0404.2010.01444.x
|
| [44] |
Holman DW, Klein RS, Ransohoff RM (2011) The blood-brain barrier, chemokines and multiple sclerosis. Biochim Biophys Acta 1812: 220-230. https://doi.org/10.1016/j.bbadis.2010.07.019
|
| [45] |
Sonar SA, Lal G (2018) Blood-brain barrier and its function during inflammation and autoimmunity. J Leukocyte Biol 103: 839-853. https://doi.org/10.1002/JLB.1RU1117-428R
|
| [46] |
Thompson AJ, Baranzini SE, Geurts J, et al. (2018) Multiple sclerosis. Lancet 391: 1622-1636. https://doi.org/10.1016/S0140-6736(18)30481-1
|
| [47] |
Piehl F (2021) Current and emerging disease-modulatory therapies and treatment targets for multiple sclerosis. J Intern Med 289: 771-791. https://doi.org/10.1111/joim.13215
|
| [48] |
Claes N, Fraussen J, Stinissen P, et al. (2015) B cells are multifunctional players in multiple sclerosis pathogenesis: Insights from therapeutic interventions. Front Immunol 6: 642. https://doi.org/10.3389/fimmu.2015.00642
|
| [49] |
Milo R (2019) Therapies for multiple sclerosis targeting B cells. Croat Med J 60: 87-98. https://doi.org/10.3325/cmj.2019.60.87
|
| [50] |
McFarland HF, Martin R (2007) Multiple sclerosis: a complicated picture of autoimmunity. Nat Immunol 8: 913-919. https://doi.org/10.1038/ni1507
|
| [51] | Inglese M (2006) Multiple sclerosis: new insights and trends. Am J Neuroradiol 27: 954-957. |
| [52] |
Voet S, Prinz M, van Loo G (2019) Microglia in central nervous system inflammation and multiple sclerosis pathology. Trends Mol Med 25: 112-123. https://doi.org/10.1016/j.molmed.2018.11.005
|
| [53] |
Strik M, Cofré Lizama LE, Shanahan CJ, et al. (2021) Axonal loss in major sensorimotor tracts is associated with impaired motor performance in minimally disabled multiple sclerosis patients. Brain Commun 3: fcab032. https://doi.org/10.1093/braincomms/fcab032
|
| [54] |
Akdis CA (2006) Allergy and hypersensitivity: mechanisms of allergic disease. Curr Opin Immunol 18: 718-726. https://doi.org/10.1016/j.coi.2006.09.016
|
| [55] | Murphy K, Travers P, Walport M (2008) Allergy and hypersensitivity. Janeway's Immunobiology. UK: Garland Science. https://doi.org/10.1007/978-3-8274-2219-4 |
| [56] |
Simon D (2018) Recent advances in clinical allergy and immunology. Int Arch Allergy Imm 177: 324-333. https://doi.org/10.1159/000494931
|
| [57] |
Kumar Y, Bhatia A (2013) Immunopathogenesis of allergic disorders: current concepts. Expert Rev Clin Immunol 9: 211-226. https://doi.org/10.1586/eci.12.104
|
| [58] | Vaillant AAJ, Vashisht R, Zito PM (2022) Immediate hypersensitivity reactions In: StatPearls, Treasure Island (FL): StatPearls Publishing. |
| [59] |
Breiteneder H, Peng YQ, Agache I, et al. (2020) Biomarkers for diagnosis and prediction of therapy responses in allergic diseases and asthma. Allergy 75: 3039-3068. https://doi.org/10.1111/all.14582
|
| [60] |
Galli SJ, Tsai M, Piliponsky AM (2008) The development of allergic inflammation. Nature 454: 445-454. https://doi.org/10.1038/nature07204
|
| [61] |
Yao Y, Chen CL, Yu D, et al. (2021) Roles of follicular helper and regulatory T cells in allergic diseases and allergen immunotherapy. Allergy 76: 456-470. https://doi.org/10.1111/all.14639
|
| [62] | Muñoz-Carrillo JL, Castro-García FP, Chávez-Rubalcaba F, et al. (2018) Immune system disorders: hypersensitivity and autoimmunity. Immunoregulatory Aspects of Immunotherapy. London: InTechOpen 1-30. https://doi.org/10.5772/intechopen.75794 |
| [63] |
Remington B, Broekman HCH, Blom WM, et al. (2018) Approaches to assess IgE mediated allergy risks (sensitization and cross-reactivity) from new or modified dietary proteins. Food Chem Toxicol 112: 97-107. https://doi.org/10.1016/j.fct.2017.12.025
|
| [64] |
Kanagaratham C, El Ansari YS, Lewis OL, et al. (2020) IgE and IgG antibodies as regulators of mast cell and basophil functions in food allergy. Front Immunol 11: 603050. https://doi.org/10.3389/fimmu.2020.603050
|
| [65] | Janeway CA, Travers P, Walport M, et al. (2001) Immunobiology: The immune system in health and disease. Effector Mechanisms in Allergic Reactions. New York: Garland Science. |
| [66] |
Kitaura J, Song J, Tsai M, et al. (2003) Evidence that IgE molecules mediate a spectrum of effects on mast cell survival and activation via aggregation of the FcepsilonRI. P Natl Acad Sci USA 100: 12911-12916. https://doi.org/10.1073/pnas.1735525100
|
| [67] |
Dispenza MC (2019) Classification of hypersensitivity reactions. Allergy Asthma Proc 40: 470-473. https://doi.org/10.2500/aap.2019.40.4274
|
| [68] |
Miyake K, Karasuyama H (2017) Emerging roles of basophils in allergic inflammation. Allergol Int 66: 382-391. https://doi.org/10.1016/j.alit.2017.04.007
|
| [69] |
Prussin C, Metcalfe DD (2006) 5. IgE, mast cells, basophils, and eosinophils. J Allergy Clin Immunol 117: S450-S456. https://doi.org/10.1016/j.jaci.2005.11.016
|
| [70] |
Sampson AP (2000) The role of eosinophils and neutrophils in inflammation. Clin Exp Allergy 30: 22-27. https://doi.org/10.1046/j.1365-2222.2000.00092.x
|
| [71] |
Drazdauskaitė G, Layhadi JA, Shamji MH (2020) Mechanisms of allergen immunotherapy in allergic rhinitis. Curr Allergy Asthma Rep 21: 2. https://doi.org/10.1007/s11882-020-00977-7
|
| [72] | Akhouri S, House SA (2022) >Allergic rhinitis In: StatPearls, Treasure Island (FL): StatPearls Publishing. |
| [73] |
Yu W, Freeland DMH, Nadeau KC (2016) Food allergy: immune mechanisms, diagnosis and immunotherapy. Nat Rev Immunol 16: 751-765. https://doi.org/10.1038/nri.2016.111
|
| [74] |
Iweala OI, Choudhary SK, Commins SP (2018) Food allergy. Curr Gastroenterol Rep 20: 17. https://doi.org/10.1007/s11894-018-0624-y
|
| [75] |
Peters RL, Krawiec M, Koplin JJ, et al. (2021) Update on food allergy. Pediatr Allergy Immu 32: 647-657. https://doi.org/10.1111/pai.13443
|
| [76] | Rehman A, Amin F, Sadeeqa S (2018) Prevalence of asthma and its management: A review. J Pak Med Assoc 68: 1823-1827. |
| [77] | Ogeyingbo OD, Ahmed R, Gyawali M, et al. (2021) The relationship between vitamin D and asthma exacerbation. Cureus 13: e17279. https://doi.org/10.1111/pai.13443 |
| [78] |
Bush A (2019) Pathophysiological mechanisms of asthma. Front Pediatr 7: 68. https://doi.org/10.3389/fped.2019.00068
|
| [79] |
Nakamura Y, Tamaoki J, Nagase H, et al. (2020) Japanese guidelines for adult asthma. Allergol Int 69: 519-548. https://doi.org/10.1016/j.alit.2020.08.001
|
| [80] |
Bilò MB, Martini M, Tontini C, et al. (2021) Anaphylaxis. Eur Ann Allergy Clin Immunol 53: 4-17. https://doi.org/10.23822/EurAnnACI.1764-1489.158
|
| [81] | Pflipsen MC, Colon KMV (2020) Anaphylaxis: recognition and management. Am Fam Physician 102: 355-362. |
| [82] |
Hackett TL, Singhera GK, Shaheen F, et al. (2011) Intrinsic phenotypic differences of asthmatic epithelium and its inflammatory responses to respiratory syncytial virus and air pollution. Am J Resp Cell Mol 45: 1090-1100. https://doi.org/10.1165/rcmb.2011-0031OC
|
| [83] |
Siddiqui S, Johansson K, Joo A, et al. (2021) Epithelial miR-141 regulates IL-13-induced airway mucus production. JCI Insight 6: e139019. https://doi.org/10.1172/jci.insight.139019
|
| [84] |
Vercelli D (2008) Discovering susceptibility genes for asthma and allergy. Nat Rev Immunol 8: 169-182. https://doi.org/10.1038/nri2257
|
| [85] |
Akdis CA (2021) Does the epithelial barrier hypothesis explain the increase in allergy, autoimmunity and other chronic conditions?. Nat Rev Immunol 21: 739-751. https://doi.org/10.1038/s41577-021-00538-7
|
| [86] |
Kansen HM, Lebbink MA, Mul J, et al. (2020) Risk factors for atopic diseases and recurrent respiratory tract infections in children. Pediatr Pulm 55: 3168-3179. https://doi.org/10.1002/ppul.25042
|
| [87] |
D'Amato G, Chong-Neto HJ, Ortega OPM, et al. (2020) The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. Allergy 75: 2219-2228. https://doi.org/10.1111/all.14476
|
| [88] |
Prescott S, Saffery R (2011) The role of epigenetic dysregulation in the epidemic of allergic disease. Clin Epigenetics 2: 223-232. https://doi.org/10.1007/s13148-011-0028-4
|
| [89] |
Wilson CB, Rowell E, Sekimata M (2009) Epigenetic control of T-helper-cell differentiation. Nat Rev Immunol 9: 91-105. https://doi.org/10.1038/nri2487
|
| [90] |
Thorburn AN, Macia L, Mackay CR (2014) Diet, metabolites, and “western-lifestyle” inflammatory diseases. Immunity 40: 833-842. https://doi.org/10.1016/j.immuni.2014.05.014
|
| [91] |
Yazdanbakhsh M, Kremsner PG, van Ree R (2002) Allergy, parasites, and the hygiene hypothesis. Science 296: 490-494. https://doi.org/10.1126/science.296.5567.490
|
| [92] |
Frei R, Lauener RP, Crameri R, et al. (2012) Microbiota and dietary interactions: an update to the hygiene hypothesis?. Allergy 67: 451-461. https://doi.org/10.1111/j.1398-9995.2011.02783.x
|
| [93] |
Raj D, Kabra SK, Lodha R (2014) Childhood obesity and risk of allergy or asthma. Immunol Allergy Clin 34: 753-765. https://doi.org/10.1016/j.iac.2014.07.001
|
| [94] |
Metsälä J, Lundqvist A, Virta LJ, et al. (2013) Mother's and offspring's use of antibiotics and infant allergy to cow's milk. Epidemiology 24: 303-309. https://doi.org/10.1097/EDE.0b013e31827f520f
|
| [95] |
Loh W, Tang MLK (2018) The epidemiology of food allergy in the global context. Int J Environ Res Public Health 15: 2043. https://doi.org/10.3390/ijerph15092043
|
| [96] |
Simon D (2019) Recent advances in clinical allergy and immunology 2019. Int Arch Allergy Imm 180: 291-305. https://doi.org/10.1159/000504364
|
| [97] |
Oro AS, Guarino TJ, Driver R, et al. (1996) Regulation of disease susceptibility: decreased prevalence of IgE-mediated allergic disease in patients with multiple sclerosis. J Allergy Clin Immunol 97: 1402-1408. https://doi.org/10.1016/S0091-6749(96)70210-5
|
| [98] |
Cameron MH, Nilsagard Y (2018) Balance, gait, and falls in multiple sclerosis. Handb Clin Neurol 159: 237-250. https://doi.org/10.1016/B978-0-444-63916-5.00015-X
|
| [99] |
Rosiak K, Zagożdżon P (2017) Quality of life and social support in patients with multiple sclerosis. Psychiatr Pol 51: 923-935. https://doi.org/10.12740/PP/64709
|
| [100] |
Beiske AG, Naess H, Aarseth JH, et al. (2007) Health-related quality of life in secondary progressive multiple sclerosis. Mult Scler 13: 386-392. https://doi.org/10.1177/13524585070130030101
|
| [101] |
Lock C, Hermans G, Pedotti R, et al. (2002) Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat Med 8: 500-508. https://doi.org/10.1038/nm0502-500
|
| [102] |
Pedotti R, DeVoss JJ, Youssef S, et al. (2003) Multiple elements of the allergic arm of the immune response modulate autoimmune demyelination. P Natl Acad Sci USA 100: 1867-1872. https://doi.org/10.1073/pnas.252777399
|
| [103] |
Musio S, Gallo B, Scabeni S, et al. (2006) A key regulatory role for histamine in experimental autoimmune encephalomyelitis: disease exacerbation in histidine decarboxylase-deficient mice. J Immunol 176: 17-26. https://doi.org/10.4049/jimmunol.176.1.17
|
| [104] |
Lapilla M, Gallo B, Martinello M, et al. (2011) Histamine regulates autoreactive T cell activation and adhesiveness in inflamed brain microcirculation. J Leukocyte Biol 89: 259-267. https://doi.org/10.1189/jlb.0910486
|
| [105] |
Gregory GD, Robbie-Ryan M, Secor VH, et al. (2005) Mast cells are required for optimal autoreactive T cell responses in a murine model of multiple sclerosis. Eur J Immunol 35: 3478-3486. https://doi.org/10.1002/eji.200535271
|
| [106] | Alonso A, Hernán MA, Ascherio A (2008) Allergy, family history of autoimmune diseases, and the risk of multiple sclerosis. Acta Neurol Scand 117: 15-20. |
| [107] |
Alonso A, Jick SS, Hernán MA (2006) Allergy, histamine 1 receptor blockers, and the risk of multiple sclerosis. Neurology 66: 572-575. https://doi.org/10.1212/01.wnl.0000198507.13597.45
|
| [108] | Karimi P, Modarresi SZ, Sahraian MA, et al. (2013) The relation of multiple sclerosis with allergy and atopy: a case control study. Iran J Allergy Asthma Immunol 12: 182-189. |
| [109] |
Krishna MT, Subramanian A, Adderley NJ, et al. (2019) Allergic diseases and long-term risk of autoimmune disorders: longitudinal cohort study and cluster analysis. Eur Respir J 54: 1900476. https://doi.org/10.1183/13993003.00476-2019
|
| [110] |
Mansouri B, Asadollahi S, Heidari K, et al. (2014) Risk factors for increased multiple sclerosis susceptibility in the Iranian population. J Clin Neurosci 21: 2207-2211. https://doi.org/10.1016/j.jocn.2014.04.020
|
| [111] |
Chen J, Taylor B, Winzenberg T, et al. (2020) Comorbidities are prevalent and detrimental for employment outcomes in people of working age with multiple sclerosis. Mult Scler 26: 1550-1559. https://doi.org/10.1177/1352458519872644
|
| [112] |
Lo LMP, Taylor BV, Winzenberg T, et al. (2021) Comorbidity patterns in people with multiple sclerosis: A latent class analysis of the Australian Multiple Sclerosis Longitudinal Study. Eur J Neurol 28: 2269-2279. https://doi.org/10.1111/ene.14887
|
| [113] |
Hill E, Abboud H, Briggs FBS (2019) Prevalence of asthma in multiple sclerosis: A United States population-based study. Mult Scler Relat Disord 28: 69-74. https://doi.org/10.1016/j.msard.2018.12.012
|
| [114] |
Sorensen A, Conway DS, Briggs FBS (2021) Characterizing relapsing remitting multiple sclerosis patients burdened with hypertension, hyperlipidemia, and asthma. Mult Scler Relat Disord 53: 103040. https://doi.org/10.1016/j.msard.2021.103040
|
| [115] |
Tremlett HL, Evans J, Wiles CM, et al. (2002) Asthma and multiple sclerosis: an inverse association in a case-control general practice population. QJM 95: 753-756. https://doi.org/10.1093/qjmed/95.11.753
|
| [116] |
Bergamaschi R, Villani S, Crabbio M, et al. (2009) Inverse relationship between multiple sclerosis and allergic respiratory diseases. Neurol Sci 30: 115-118. https://doi.org/10.1007/s10072-009-0036-8
|
| [117] |
Sahraian MA, Jafarian S, Sheikhbahaei S, et al. (2013) Respiratory tract rather than cutaneous atopic allergy inversely associate with multiple sclerosis: a case-control study. Clin Neurol Neurosur 115: 2099-2102. https://doi.org/10.1016/j.clineuro.2013.07.028
|
| [118] |
Pedotti R, Farinotti M, Falcone C, et al. (2009) Allergy and multiple sclerosis: a population-based case-control study. Mult Scler 15: 899-906. https://doi.org/10.1177/1352458509106211
|
| [119] |
Ren J, Ni H, Kim M, et al. (2017) Allergies, antibiotics use, and multiple sclerosis. Curr Med Res Opin 33: 1451-1456. https://doi.org/10.1080/03007995.2017.1325575
|
| [120] |
Skaaby T, Husemoen LL, Thuesen BH, et al. (2015) Specific IgE positivity against inhalant allergens and development of autoimmune disease. Autoimmunity 48: 282-288. https://doi.org/10.3109/08916934.2014.1003640
|
| [121] |
Manouchehrinia A, Edwards LJ, Roshanisefat H, et al. (2015) Multiple sclerosis course and clinical outcomes in patients with comorbid asthma: a survey study. BMJ Open 5: e007806. https://doi.org/10.1136/bmjopen-2015-007806
|
| [122] |
Ponsonby AL, Dwyer T, van der Mei I, et al. (2006) Asthma onset prior to multiple sclerosis and the contribution of sibling exposure in early life. Clin Exp Immunol 146: 463-470. https://doi.org/10.1111/j.1365-2249.2006.03235.x
|
| [123] |
Hughes AM, Lucas RM, McMichael AJ, et al. (2013) Early-life hygiene-related factors affect risk of central nervous system demyelination and asthma differentially. Clin Exp Immunol 172: 466-474. https://doi.org/10.1111/cei.12077
|
| [124] | Lopez CM, Yarrarapu SNS, Mendez MD (2022) Food allergies In: StatPearls, Treasure Island (FL): StatPearls Publishing. |
| [125] |
Yu JW, Pekeles G, Legault L, et al. (2006) Milk allergy and vitamin D deficiency rickets: a common disorder associated with an uncommon disease. Ann Allerg Asthma Im 96: 615-619. https://doi.org/10.1016/S1081-1206(10)63558-2
|
| [126] |
Correale J, Ysrraelit MC, Gaitán MI (2009) Immunomodulatory effects of vitamin D in multiple sclerosis. Brain 132: 1146-1160. https://doi.org/10.1093/brain/awp033
|
| [127] |
Ramagopalan SV, Dyment DA, Guimond C, et al. (2010) Childhood cow's milk allergy and the risk of multiple sclerosis: a population based study. J Neurol Sci 291: 86-88. https://doi.org/10.1016/j.jns.2009.10.021
|
| [128] | Pichler J, Gerstmayr M, Szepfalusi Z, et al. (2002) 1 alpha,25(OH)2D3 inhibits not only Th1 but also Th2 differentiation in human cord blood T cells. Pediatr Res 52: 12-18. https://doi.org/10.1203/00006450-200207000-00005 |
| [129] |
Sakaguchi S, Yamaguchi T, Nomura T, et al. (2008) Regulatory T cells and immune tolerance. Cell 133: 775-787. https://doi.org/10.1016/j.cell.2008.05.009
|
| [130] |
Mirzakhani H, Al-Garawi A, Weiss ST, et al. (2015) Vitamin D and the development of allergic disease: how important is it?. Clin Exp Allergy 45: 114-125. https://doi.org/10.1111/cea.12430
|
| [131] |
Danikowski KM, Jayaraman S, Prabhakar BS (2017) Regulatory T cells in multiple sclerosis and myasthenia gravis. J Neuroinflammation 14: 117. https://doi.org/10.1186/s12974-017-0892-8
|
| [132] | Ashtari F, Jamshidi F, Shoormasti RS, et al. (2013) Cow's milk allergy in multiple sclerosis patients. J Res Med Sci 18: S62-S65. |
| [133] | Ashtari F, Jamshidi F, Shoormasti RS, et al. (2013) Fish and egg specific immunoglobin e in multiple sclerosis patients. Int J Prev Med 4: S185-S188. |
| [134] |
Bourne T, Waltz M, Casper TC, et al. (2017) Evaluating the association of allergies with multiple sclerosis susceptibility risk and disease activity in a pediatric population. J Neurol Sci 375: 371-375. https://doi.org/10.1016/j.jns.2017.02.041
|
| [135] |
Fakih R, Diaz-Cruz C, Chua AS, et al. (2019) Food allergies are associated with increased disease activity in multiple sclerosis. J Neurol Neurosur Ps 90: 629-635. https://doi.org/10.1136/jnnp-2018-319301
|
| [136] |
Albatineh AN, Alroughani R, Al-Temaimi R (2020) Predictors of multiple sclerosis severity score in patients with multiple sclerosis. Int J MS Care 22: 233-238. https://doi.org/10.7224/1537-2073.2019-054
|
| [137] |
Parodi B, de Rosbo NK (2021) The gut-brain axis in multiple sclerosis. Is its dysfunction a pathological trigger or a consequence of the disease?. Front Immunol 12: 718220. https://doi.org/10.3389/fimmu.2021.718220
|
| [138] |
Agirman G, Yu KB, Hsiao EY (2021) Signaling inflammation across the gut-brain axis. Science 374: 1087-1092. https://doi.org/10.1126/science.abi6087
|
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Figures(1) / Tables(2)
Lisa Aielli, Federica Serra, Erica Costantini. Multiple sclerosis and allergic diseases: is there a relationship?[J]. AIMS Allergy and Immunology, 2022, 6(3): 126-152. doi: 10.3934/Allergy.2022011
| Tag | Drug class | Number of drugs |
| C1 | Anti-allergic agent | 35 |
| C2 | Anti-inflammatory | 116 |
| C3 | Antibacterial | 99 |
| C4 | Antidiabetic agent | 25 |
| C5 | Antifungal | 21 |
| C6 | Antineoplastic | 51 |
| C7 | Antiviral | 44 |
| C8 | Cardiovascular agent | 54 |
| C9 | Endocrine and hormonal agent | 19 |
| C10 | Gastrointestinal agent | 29 |
| C11 | Hypolipidemic agent | 13 |
| C12 | Immunological agent | 8 |
| C13 | Neuropsychiatric agent | 37 |
| C14 | Ophthalmic agent | 28 |
| Total | 579 | |
DownLoad:
CSV
| Classification algorithm | SMOTE | ACC | Weighted Precision | Weighted F-measure | Weighted MCC | Weighted AUROC | Weighted AUPR |
| Random forest | √ | 0.9486 | 0.9631 | 0.9511 | 0.9484 | 0.9958 | 0.9830 |
| Support vector machine | √ | 0.9583 | 0.9752 | 0.9640 | 0.9625 | 0.9830 | 0.9350 |
| Random forest | ⊆ | 0.9410 | - | - | - | 0.9930 | 0.9710 |
| Support vector machine | ⊆ | 0.9600 | 0.9610 | 0.9600 | 0.9550 | 0.9840 | 0.9320 |
DownLoad:
CSV
| Tag of class | Recall(i) | Precision(i) | F-measure(i) | MCC(i) | AUROC(i) | AUPR(i) |
| C1 | 0.9052 | 0.9319 | 0.9141 | 0.9162 | 0.9975 | 0.9627 |
| C2 | 0.9550 | 0.9449 | 0.9469 | 0.9366 | 0.9972 | 0.9879 |
| C3 | 0.9983 | 0.9761 | 0.9865 | 0.9845 | 0.9998 | 0.9991 |
| C4 | 0.9750 | 0.9749 | 0.9690 | 0.9708 | 0.9994 | 0.9921 |
| C5 | 0.9676 | 1.0000 | 0.9804 | 0.9812 | 0.9943 | 0.9780 |
| C6 | 0.9473 | 0.9113 | 0.9179 | 0.9182 | 0.9915 | 0.9770 |
| C7 | 0.9434 | 0.9611 | 0.9450 | 0.9453 | 0.9932 | 0.9769 |
| C8 | 0.9377 | 0.9829 | 0.9548 | 0.9537 | 0.9940 | 0.9709 |
| C9 | 0.9191 | 0.9294 | 0.9499 | 0.9511 | 0.9972 | 0.9659 |
| C10 | 0.9132 | 0.8882 | 0.8858 | 0.8886 | 0.9969 | 0.9602 |
| C11 | 0.8870 | 0.8528 | 0.9582 | 0.9622 | 1.0000 | 1.0000 |
| C12 | 0.2726 | 0.5183 | 0.5834 | 0.5852 | 0.9127 | 0.7654 |
| C13 | 0.9781 | 0.9456 | 0.9552 | 0.9557 | 0.9997 | 0.9958 |
| C14 | 0.9831 | 0.9738 | 0.9768 | 0.9764 | 0.9998 | 0.9967 |
DownLoad:
CSV
| Tag of class | Recall(i) | Precision(i) | F-measure(i) | MCC(i) | AUROC(i) | AUPR(i) |
| C1 | 0.9444 | 0.9152 | 0.9211 | 0.9215 | 0.9934 | 0.8984 |
| C2 | 0.9635 | 0.9584 | 0.9593 | 0.9513 | 0.9882 | 0.9428 |
| C3 | 0.9967 | 0.9803 | 0.9877 | 0.9860 | 0.9982 | 0.9803 |
| C4 | 0.9717 | 1.0000 | 0.9831 | 0.9838 | 0.9982 | 0.9836 |
| C5 | 0.9676 | 0.9867 | 0.9704 | 0.9725 | 0.9916 | 0.9603 |
| C6 | 0.9556 | 0.9656 | 0.9549 | 0.9540 | 0.9857 | 0.9604 |
| C7 | 0.9384 | 0.9598 | 0.9407 | 0.9412 | 0.9700 | 0.9197 |
| C8 | 0.9569 | 0.9805 | 0.9650 | 0.9637 | 0.9905 | 0.9546 |
| C9 | 0.9109 | 1.0000 | 0.9756 | 0.9759 | 0.9964 | 0.9644 |
| C10 | 0.8567 | 0.9282 | 0.9049 | 0.9096 | 0.9623 | 0.8393 |
| C11 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| C12 | 0.7373 | 0.4740 | 0.6904 | 0.7134 | 0.9270 | 0.5556 |
| C13 | 0.9790 | 0.9843 | 0.9786 | 0.9788 | 0.9993 | 0.9814 |
| C14 | 0.9841 | 0.9944 | 0.9874 | 0.9875 | 0.9985 | 0.9874 |
DownLoad:
CSV
| Drug features | Classification algorithm | ACC | Weighted Precision | Weighted F-measure | Weighted MCC | Weighted AUROC | Weighted AUPR |
| Drug features produced by Mashup | Random forest | 0.8891 | 0.9282 | 0.9069 | 0.9006 | 0.9819 | 0.9359 |
| Support vector machine | 0.9526 | 0.9724 | 0.9617 | 0.9579 | 0.9880 | 0.9280 | |
| Drug fingerprint features | Random forest | 0.8432 | 0.8768 | 0.8431 | 0.8366 | 0.9748 | 0.9107 |
| Support vector machine | 0.8426 | 0.8791 | 0.8489 | 0.8420 | 0.9476 | 0.7943 |
DownLoad:
CSV
| Tag | Drug class | Number of drugs |
| C1 | Anti-allergic agent | 35 |
| C2 | Anti-inflammatory | 116 |
| C3 | Antibacterial | 99 |
| C4 | Antidiabetic agent | 25 |
| C5 | Antifungal | 21 |
| C6 | Antineoplastic | 51 |
| C7 | Antiviral | 44 |
| C8 | Cardiovascular agent | 54 |
| C9 | Endocrine and hormonal agent | 19 |
| C10 | Gastrointestinal agent | 29 |
| C11 | Hypolipidemic agent | 13 |
| C12 | Immunological agent | 8 |
| C13 | Neuropsychiatric agent | 37 |
| C14 | Ophthalmic agent | 28 |
| Total | 579 | |
| Classification algorithm | SMOTE | ACC | Weighted Precision | Weighted F-measure | Weighted MCC | Weighted AUROC | Weighted AUPR |
| Random forest | √ | 0.9486 | 0.9631 | 0.9511 | 0.9484 | 0.9958 | 0.9830 |
| Support vector machine | √ | 0.9583 | 0.9752 | 0.9640 | 0.9625 | 0.9830 | 0.9350 |
| Random forest | ⊆ | 0.9410 | - | - | - | 0.9930 | 0.9710 |
| Support vector machine | ⊆ | 0.9600 | 0.9610 | 0.9600 | 0.9550 | 0.9840 | 0.9320 |
| Tag of class | Recall(i) | Precision(i) | F-measure(i) | MCC(i) | AUROC(i) | AUPR(i) |
| C1 | 0.9052 | 0.9319 | 0.9141 | 0.9162 | 0.9975 | 0.9627 |
| C2 | 0.9550 | 0.9449 | 0.9469 | 0.9366 | 0.9972 | 0.9879 |
| C3 | 0.9983 | 0.9761 | 0.9865 | 0.9845 | 0.9998 | 0.9991 |
| C4 | 0.9750 | 0.9749 | 0.9690 | 0.9708 | 0.9994 | 0.9921 |
| C5 | 0.9676 | 1.0000 | 0.9804 | 0.9812 | 0.9943 | 0.9780 |
| C6 | 0.9473 | 0.9113 | 0.9179 | 0.9182 | 0.9915 | 0.9770 |
| C7 | 0.9434 | 0.9611 | 0.9450 | 0.9453 | 0.9932 | 0.9769 |
| C8 | 0.9377 | 0.9829 | 0.9548 | 0.9537 | 0.9940 | 0.9709 |
| C9 | 0.9191 | 0.9294 | 0.9499 | 0.9511 | 0.9972 | 0.9659 |
| C10 | 0.9132 | 0.8882 | 0.8858 | 0.8886 | 0.9969 | 0.9602 |
| C11 | 0.8870 | 0.8528 | 0.9582 | 0.9622 | 1.0000 | 1.0000 |
| C12 | 0.2726 | 0.5183 | 0.5834 | 0.5852 | 0.9127 | 0.7654 |
| C13 | 0.9781 | 0.9456 | 0.9552 | 0.9557 | 0.9997 | 0.9958 |
| C14 | 0.9831 | 0.9738 | 0.9768 | 0.9764 | 0.9998 | 0.9967 |
| Tag of class | Recall(i) | Precision(i) | F-measure(i) | MCC(i) | AUROC(i) | AUPR(i) |
| C1 | 0.9444 | 0.9152 | 0.9211 | 0.9215 | 0.9934 | 0.8984 |
| C2 | 0.9635 | 0.9584 | 0.9593 | 0.9513 | 0.9882 | 0.9428 |
| C3 | 0.9967 | 0.9803 | 0.9877 | 0.9860 | 0.9982 | 0.9803 |
| C4 | 0.9717 | 1.0000 | 0.9831 | 0.9838 | 0.9982 | 0.9836 |
| C5 | 0.9676 | 0.9867 | 0.9704 | 0.9725 | 0.9916 | 0.9603 |
| C6 | 0.9556 | 0.9656 | 0.9549 | 0.9540 | 0.9857 | 0.9604 |
| C7 | 0.9384 | 0.9598 | 0.9407 | 0.9412 | 0.9700 | 0.9197 |
| C8 | 0.9569 | 0.9805 | 0.9650 | 0.9637 | 0.9905 | 0.9546 |
| C9 | 0.9109 | 1.0000 | 0.9756 | 0.9759 | 0.9964 | 0.9644 |
| C10 | 0.8567 | 0.9282 | 0.9049 | 0.9096 | 0.9623 | 0.8393 |
| C11 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| C12 | 0.7373 | 0.4740 | 0.6904 | 0.7134 | 0.9270 | 0.5556 |
| C13 | 0.9790 | 0.9843 | 0.9786 | 0.9788 | 0.9993 | 0.9814 |
| C14 | 0.9841 | 0.9944 | 0.9874 | 0.9875 | 0.9985 | 0.9874 |
| Drug features | Classification algorithm | ACC | Weighted Precision | Weighted F-measure | Weighted MCC | Weighted AUROC | Weighted AUPR |
| Drug features produced by Mashup | Random forest | 0.8891 | 0.9282 | 0.9069 | 0.9006 | 0.9819 | 0.9359 |
| Support vector machine | 0.9526 | 0.9724 | 0.9617 | 0.9579 | 0.9880 | 0.9280 | |
| Drug fingerprint features | Random forest | 0.8432 | 0.8768 | 0.8431 | 0.8366 | 0.9748 | 0.9107 |
| Support vector machine | 0.8426 | 0.8791 | 0.8489 | 0.8420 | 0.9476 | 0.7943 |
