Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

Marios G. Krokidis; Themis P. Exarchos; Panagiotis Vlamos; Marios G. Krokidis; Themis P. Exarchos; Panagiotis Vlamos

doi:10.3934/mbe.2021094

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 2: 1813-1832. doi: 10.3934/mbe.2021094

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Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Greece

Received: 29 November 2020 Accepted: 07 February 2021 Published: 22 February 2021

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.
- neurogenerative diseases,
- omics,
- Alzheimer's disease,
- Parkinson's disease,
- neuronal loss,
- data analysis,
- biomarkers
Citation: Marios G. Krokidis, Themis P. Exarchos, Panagiotis Vlamos. Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1813-1832. doi: 10.3934/mbe.2021094

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Abstract

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

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