Manuscript Topics

With comprehensive biological data of Genomics, Transcriptomics, Proteomics and Interactomics, machine learning provides quantitative insights into molecular biology. Methods and tools using machine learning largely expand the existed biological knowledge in exploring and interpreting sophisticate biological mechanisms, inferring underling relationships and interactions, predicting consequences from disturbance and building hypothesis in molecular biological systems.

This special issue aims to cover the cutting-edge capabilities in machine learning methods and applications in the research field of molecular biology. Topics that will be considered for this special issue should be new or improvement of machine learning methods and applications addressing bioinformatics challenges and fundamental molecular biological problems. Topics of interest include, but are not limited to:

• Studies on biological data modeling and simulation in machine learning
• Functional annotating and prediction on DNA, gene, RNA, protein
• Molecular biological mechanism modeling and prediction on regulation, post-translation and methylation
• Protein structure and protein-protein interaction modeling and prediction
• Drug target modelling and prediction

There is no page limit. All papers will be promptly and carefully refereed and selected based on the methods contribution and biological relevance.

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