Arjun Krishnan

@compbiologist.bsky.social

1105 Followers 168 Following

ML/AI methods & tools for using massive public data collections to gain insights into complex disease mechanisms. Associate Professor & Group leader thekrishnanlab.org at the Dept. of Biomedical Informatics at CU Anschutz.

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HMGP 7622 Rethinking Data Analysis — A researcher’s guide to avoiding missteps and misuse Feb 3 – May 5, 2026 | Tue 2–3:30p OVERVIEW This is a short (1-credit) course designed to: 1) Discuss common misunderstandings & typical errors in the practice of statistical data analysis. 2) Provide a mental toolkit for critically thinking about statistical methods and results. TOPICS Estimating error, uncertainty • Underpowered statistics • Multiple testing • P-hacking • Pseudoreplication • Regression to the mean • Double dipping • Spurious associations • Visualization challenges • Reproducibility, replicability PREREQUISITES 1) Introductory knowledge of statistics & probability 2) Introductory experience with data wrangling, analysis, & visualization using R/Python. INSTRUCTOR Arjun Krishnan Associate Professor, Department of Biomedical Informatics University of Colorado Anschutz Medical Campus arjun.krishnan@cuanschutz.edu | @compbiologist | thekrishnanlab.org
Figure depicting how traditional methods rely on homology to determine functional equivalence and, by contrast, modern data-driven approaches use what we call 'agnology', identifying fully or partially functionally equivalent genes regardless of evolutionary origin, allowing for ambiguity in homology or analogy.
Figure depicting the four classes of important questions that frequently arise when using research organisms to study biomedical questions and translating findings to humans. a, How to predict disease–gene or function–gene relationships across species? Diagram depicts genes in each species associating with specific functions, diseases and phenotypes. b, How to identify functionally equivalent molecular components across species? Diagram depicts finding the most equivalent gene, pathway or expression module or phenotype between species. c, How to infer perturbed molecular profiles across species? Diagram depicts gene expression in each species as a result of taking a particular perturbation like a drug. d, How to map equivalent cell types and cell states across species? Diagram depicts alignment of cell types across species. This Perspective comprehensively lays out the landscape of recent and state-of-the-art data-driven strategies, including those that leverage artificial intelligence and machine learning (ML), for answering these questions.
Graphical abstract of the review article that pictorially summarizes the five major themes: 1) Network inference and comparison, 2) Multimodal data integration and heterogeneous networks, 3) Higher-order network analysis, 4) Machine learning on networks, and 5) Network-based personalized medicine.