Manuscript Topics

Artificial Neural Networks (ANNs) are computational models that mimic the structure and function of the human brain. They have been used extensively in a wide range of fields, including biosciences and engineering, to solve complex problems that are difficult to address with traditional analytical methods. In biosciences, ANNs have been used for applications such as drug discovery, disease diagnosis, and medical imaging analysis. In engineering, they have been applied in fields such as control systems, optimization, and prediction.

One of the key advantages of ANNs is their ability to learn from data, which enables them to make accurate predictions and classifications. They can also be used to model complex nonlinear relationships, which are often present in biological and engineering systems. Additionally, ANNs can be trained to detect patterns and anomalies in large datasets, which can be useful in identifying biomarkers or predicting outcomes.

Overall, ANNs have shown great potential in biosciences and engineering, and their use is likely to continue to grow as more data becomes available and computational resources continue to improve.

Topics include but are not limited to:

• Introduction to Artificial Neural Networks (ANNs)
• Applications of ANNs in Biosciences
• Applications of ANNs in Engineering
• ANNs for Drug Discovery
• ANNs for Disease Diagnosis
• ANNs for Medical Imaging Analysis
• ANNs for Control Systems
• ANNs for Optimization
• ANNs for Prediction
• Challenges in ANNs for Biosciences and Engineering
• ANNs for Biomarker Identification
• ANNs for Predictive Modeling
• ANNs for Anomaly Detection
• ANNs for fault diagnosis
• Interpreting ANNs in Biosciences and Engineering
• Future Directions of ANNs in Biosciences and Engineering
• Deep learning-based methods for Biosciences and Engineering

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