Understanding information at the biomolecular level using statistics and machine learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
biology, statistics, machine learning, genomics, Central Dogma, DNA, RNA, protein, information, genome editing, CRISPR, next-generation sequencing, deep learning, genetic engineering, keratinocyte, cancer, immune system, CRISPR base editor, squamous cell carcinoma, basal cell carcinoma, RNA-Seq, scRNA-Seq, convolutional neural network, deep metric learning, multimodal learning, T-Cell receptors, neural network interpretation, epitope, MCMC
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign