Stars
This package contains deep learning models and related scripts for RoseTTAFold
A deep learning package for many-body potential energy representation and molecular dynamics
sensitive and precise assembly of short sequencing reads
MMseqs2: ultra fast and sensitive search and clustering suite
GREMLIN - learn MRF/potts model from input multiple sequence alignment! Implementation now available in C++ and Tensorflow/Python!
Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design
P2Rank: Protein-ligand binding site prediction from protein structure based on machine learning.
A collaborative list of awesome CryoEM (Cryo Electron Microscopy) resources.
Implementation of benchmark RL algorithms
Given CA trace in PDB format, restore full atoms
A basecaller for Oxford Nanopore Technologies' sequencers
Predict the boundary of transcript start and end from RNA-seq reads alignment
Continuous Wavelet Dynamic Time Warping for unbalanced global mapping in nanopore sequencing analysis
CURL command to call RaptorX-Property and RaptorX-Contact locally
DeepAlign: the protein structure analysis toolkit
Extract structural features from original PDB file
Continuous Wavelet Dynamic Time Warping for unbalanced global mapping of two signals
Training Conditional Random Fields (CRF) by Maximizing Area Under the ROC Curve (AUC)
Stand-alone software package of PureseqTM for transmembrane topology prediction from amino acid sequence only.
A Web Server for transmembrane topology prediction from amino acid sequence only.
The datasets for training and testing PureseqTM.
Predict protein local properties using sequence or profile information.
Generate A3M and TGT file from a given sequence in FASTA format.
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Linear-chain LSTM-CRFs and Convolutional CRFs in PyTorch.
(Linear-chain) Conditional random field in PyTorch.
PyTorch implementations of protein secondary structure prediction on CB513.
Empower Sequence Labeling with Task-Aware Neural Language Model | a PyTorch Tutorial to Sequence Labeling