sokrypton 的倉庫

sokrypton/7.571Jupyter Notebook

7.571 - Quantitative Analysis of Biological Data Material

最近提交 2026年3月10日

(4 stars) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/AM216Jupyter Notebook

Material for lecture

最近提交 2024年8月14日

(1 star) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/AccAdam_TF2Python

TF2-compatible Accumulated Gradients for Adam

最近提交 2022年3月18日

(1 star) (1 fork) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/AfDesign_partialJupyter Notebook

WIP

最近提交 2022年7月7日

(2 stars) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/CIRPINJupyter Notebook

Source code for CIRPIN: Learning Circular Permutation-Invariant Representations to Uncover Putative Protein Homologs

最近提交 2025年12月4日

(2 stars) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/ChaiFolder

最近提交 2026年3月13日

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sokrypton/ColabBioJupyter Notebook

random colab notebooks for biology

最近提交 2025年8月23日

(26 stars) (3 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/ColabDesignPython

Making Protein Design accessible to all via Google Colab!

最近提交 2025年10月23日

(914 stars) (224 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/ColabDesign_paramsJupyter Notebook

params moved to seperate repo

最近提交 2023年7月14日

(1 star) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/ColabFoldJupyter Notebook

Making Protein folding accessible to all!

最近提交 2026年4月29日

(2,776 stars) (722 forks) (2 個已索引 issue) (2 個開放 good first issue)

sokrypton/DeepLearningExamplesJupyter Notebook

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

最近提交 2023年5月22日

(2 stars) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/EMBOIntegModelling23Jupyter Notebook

最近提交 2023年9月22日

(2 stars) (0 forks) (0 個已索引 issue) (0 個開放 good first issue)

sokrypton/GREMLINMATLAB

GREMLIN is a method to learn a statistical model of a protein family that captures both conservation and co-evolution patterns in the family. The strength of measured co-evolution is strongly predictive of residue-residue contacts in the 3D structure of the protein.

最近提交 2016年7月3日