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songlab-cal

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This repository contains code for reproducing results in our paper Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention

tape

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

Python

636

gpn

Genomic Pre-trained Network

Jupyter Notebook

182

tape-neurips2019

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. (DEPRECATED)

Python

118

mogwai

Python

rna-sieve

A library for the deconvolution of bulk cell samples using single-cell RNA expression data.

Mathematica

CPT

Cross-protein transfer learning for variant effect prediction

Jupyter Notebook

scquint

Jupyter Notebook

swga2

Python

CherryML

Scalable Maximum Likelihood Estimation of Phylogenetic Models

Python

scquint-analysis

Python

MultiCluster

Software for three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition

MATLAB

contact-geometry

Python

MOCHIS

One- and two-sample tests against general families of alternatives

Mathematica

HiDENSEC

Code for Tracing cancer evolution and heterogeneity using Hi-C

Jupyter Notebook

flinty

A Simple and Flexible Test of Sample Exchangeability with Applications to Statistical Genomics

Python

slc22a5

Variant effect prediction for SLC22A5 transporter gene using Potts models

Jupyter Notebook

EGGTART

Extensive GUI gives TASEP realization in real-time.

songlab-cal/factored-attention

songlab-cal

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