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MIT Course announcement: Machine Learning for Computational Biologyhashtag#MLCB25 Fall'24 Lecture Videos:https://lnkd.in/efSvp7hY Fall'24 Lecture Notes:https://lnkd.in/eWBAxQHk (a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks. (b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning. (c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification. (d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology. Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery. First Lecture: Thu Sept 4 at 1pm in 32-144 With: Prof.Manolis Kellis, Prof.Eric Alm, TAs:Ananth Shyamal,Shitong Luo Course website:https://lnkd.in/eemavz6J
Fall'24 Lecture Videos: https://lnkd.in/efSvp7hY
Fall'24 Lecture Notes: https://lnkd.in/eWBAxQHk
(a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks.
(b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning.
(c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification.
(d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology.
Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery.
First Lecture: Thu Sept 4 at 1pm in 32-144
With: Prof. Manolis Kellis, Prof. Eric Alm, TAs: Ananth Shyamal, Shitong Luo
Course website: https://lnkd.in/eemavz6J