Integration of Omics Data with Machine Learning and Literature Mining: An Accurate and Fast Approach in Drug Repurposing and Comprehensive Systems Biology

Dear Colleagues,

The development and widespread use of next-generation sequencing (NGS) technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale datasets that opened up a vast number of possible applications for data-driven methodologies. These applications range from fundamental research to clinical applications.

However, NGS-based studies usually utilized small sample sizes, and thus the results of many kinds of this research gave surprisingly low reproducibility rates in the studies that followed. Hence, the meta-analysis of available data from several studies was soon identified as the appropriate technique to obtain adequate sample sizes and optimal strength for the detection of genetic associations.

On the other hand, data-driven NGS research areas have tailored data mining technologies such as machine learning. A machine learning model, which is considered as the most crucial technology for efficient pattern discovery, can incorporate prior knowledge from different omics data. Subsequently, the models can identify hidden knowledge, patterns, and relationships in an enormous amount of NGS information.

The combination of machine learning and meta-analysis in the analysis of multiple NGS experiments simultaneously may open up a new avenue to use publicly available data to better uncover key molecular factors and their underlying mechanisms in omics studies.

Dr. Esmaeil Ebrahimie
Prof. Dr. Matthias Kohl
Dr. James Perkins
Guest Editors

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