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Cell Systems|May 13, 2025
Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screensDaniel Paysan, Adityanarayanan Radhakrishnan, Xinyi Zhang, et al.Scientific Reports|December 22, 2017
Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer DiagnosisAdityanarayanan Radhakrishnan, Karthik Damodaran, Ali C Soylemezoglu, et al.Proceedings of the National Academy of Sciences of the United States of America|July 26, 2019
Reconciling modern machine-learning practice and the classical bias-variance trade-offMikhail Belkin, Daniel Hsu, Siyuan Ma, et al.Nature Communications|February 16, 2021
Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposingAnastasiya Belyaeva, Louis Cammarata, Adityanarayanan Radhakrishnan, et al.Journal of Neurodevelopmental Disorders|June 18, 2014
Robust features for the automatic identification of autism spectrum disorder in childrenJustin Eldridge, Alison E Lane, Mikhail Belkin, et al.Plos Computational Biology|March 30, 2026
CAPYBARA: A generalizable framework for predicting serological measurements across human cohortsSierra Orsinelli-Rivers, Daniel Beaglehole, Tal EinavPlos One|May 15, 2014
The geometry and dynamics of lifelogs: discovering the organizational principles of human experienceVishnu Sreekumar, Simon Dennis, Isidoros Doxas, et al.Nature Communications|January 5, 2021
Multi-domain translation between single-cell imaging and sequencing data using autoencodersKarren Dai Yang, Anastasiya Belyaeva, Saradha Venkatachalapathy, et al.Nature Communications|April 27, 2023
Cross-modal autoencoder framework learns holistic representations of cardiovascular stateAdityanarayanan Radhakrishnan, Sam F Friedman, Shaan Khurshid, et al.Journal of the American Chemical Society|July 6, 2006
Toward chiral sum-frequency spectroscopyNa Ji, Victor Ostroverkhov, Mikhail Belkin, et al.Pageof 3