About this Research Topic

Background

We are pleased to announce this Research Topic to highlight studies that were presented at the 18th annual conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS) , held virtually on April 25 -27, 2022. The conference program includes three keynote speakers, 12 breakout sessions, and two hands-on workshops.

The Research Topic will focus on late-breaking research on data science and artificial intelligence applied to disease biology modeling, genomics, systems biology, translational bioinformatics, computational drug development, and clinical informatics applications to improve public health. Various article types of articles are welcomed (e.g. original research, review, short review, methods, etc.) and specific areas of interest include, but are not limited to:

• Application of Machine Learning in Components of Predictive Public Health
• Artificial Intelligence (AI) Shakes Up Drug Discovery
• Barriers and Pitfalls of AI Application in Medicine
• Cancer Informatics
• Characterization of Microbiome Ecology and Pathogen Virulence via AI/ML
• Computational Approaches in Decoding Tumor Immune
• Microenvironment for Precision Oncology
• Data-Centric AI for Regulatory Research
• Liquid Biopsy Science
• Machine Learning and Single Cell Genomics in Public Health
• Machine Learning Applications in Toxicology
• Network Biology and Medicine
• Predicting, Modelling, and Testing of Drug Combinations in Cancer
• Terminologies and Standards in Big Data Analysis
• Text Analytics for Drug safety and Health Data
• Toxicogenomics in Assessment of Pharmacological and Environmental Toxicity

Keywords: machine learning for public health, precision medicine, network biology, network medicine, big data for drug safety, big data for toxicity

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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