AI/ML to Infer Biology from Sequencing Data

DNA can be considered the oldest language, written through the chemistry of life.

The application of Artificial Intelligence (AI) and Machine Learning (ML) in deciphering biological insights from sequencing data represents a transformative shift in the field of Computational Biology. These computational technologies can handle the enormous volumes of data generated by sequencing platforms, from DNA and RNA to more complex proteomic sequences. Traditional analytical methods often fall short of capturing the intricate patterns and relationships hidden in this data. AI/ML algorithms, however, excel in identifying these subtle connections, enabling more accurate predictions and fostering deeper understanding of biological processes. We are developing AI and ML models and tools for applications ranging from profiling microbial species and biosynthetic gene clusters (BGCs), identifying genetic markers for diseases, testing for rare diseases, understanding evolutionary pathways, to even the development of personalized medicine.

Detecting plasmid vs. chromosome DNA sequences from short reads

Publications

  1. Rahnavard A, Omics correlation for efficient network construction Nature Computational Science (April 2023)

  2. Baghbanzadeh M, Dawson T, Sayoldin B, Oakley T, Crandall K, Rahnavard A deepBreaks: a Machine Learning Tool for Identifying and Prioritizing Genotype-phenotype Associations Preprint on Research Square (February 2023)

  3. Rahnavard A, Chatterjee R, Wen H, Gaylord C, Mugusi S, Klatt KC, Smith ER Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges Preprint on Research Square (November 2022)

  4. Rahnavard A, Mann B, Giri A, Chatterjee R, Crandall KA
    Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity Scientific Reports (July 2022)

  5. Mallick H, Chatterjee S, Chowdhury S, Chatterjee S, Rahnavard A, Hicks, S.C Differential expression of single‐cell RNA‐seq data using Tweedie models
    Statistics in Medicine (June 2022)

  6. Rahnavard A, Dawson T, Clement R, Stearrett N, Pérez-Losada M, Crandall KA Epidemiological associations with genomic variation in SARS-CoV-2 Scientific Reports (November 2021)

  7. Rahnavard A, Chatterjee S, Sayoldin B, Crandall KA, Tekola-Ayele F, Mallick H. Omics community detection using multi-resolution clustering
    Bioinformatics (October 2021)