softbear

softbear's Stars

• bentrevett/pytorch-seq2seq

  Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

  Language:Jupyter Notebook5.2k1.3k

• networkinference/ARNI

  Algorithm for Revealing Network Interactions (ARNI) from nonlinear collective dynamics

  Language:HTML259

• bnusss/NGNC

  Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

  Language:Python21

• bnusss/GGN

  Gumbel Graph Network (GGN) : A General Deep Learning Framework for Network Reconstruction

  Language:Python6822

• NeuromatchAcademy/course-content

  NMA Computational Neuroscience course

  Language:Jupyter Notebook2.6k967

• firepear/homefarm

  Tools for deploying and managing a BOINC compute farm

  Language:Shell23

• cjmielke/quarantineAtHome

  quarantine@Home

  Language:Python143

• mhallsmoore/qstrader

  QuantStart.com - QSTrader backtesting simulation engine.

  Language:Python2.8k841

• google/in-silico-labeling

  Code for In silico labeling: Predicting fluorescent labels in unlabeled images

  Language:Python25880

• aganezov/RCK

  RCK: Reconstruction of clone- and haplotype-specific Cancer Karyotypes

  Language:Python164

• bcgsc/ntJoin

  🔗Genome assembly scaffolder using minimizer graphs

  Language:Python8015

• mila-iqia/ivado-mila-dl-school-2019-vancouver

  Language:Jupyter Notebook1110

• mila-iqia/training

  Language:Python87

• vacquaviva/MLSummerSchool_CCA19

  Materials for the Mini Summer School in Machine Learning x Astro taught at CCA in Summer '19

  Language:Jupyter Notebook156

• bpucker/APPLS

  Applied Python Programming for Life Scientists

  Language:Python2511

• opencobra/memote

  memote – the genome-scale metabolic model test suite

  Language:HTML12326

• ELELAB/MoonlightR

  Language:R176

• dwgoon/sfa

  A simulation framework to analyze signal flows in complex networks

  Language:Python135

• aqlaboratory/hsm

  Code associated with "Biophysical prediction of protein-peptide interactions and signaling networks using machine learning."

  Language:Jupyter Notebook6722

• facebookresearch/PoincareMaps

  The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method harnessing the power of hyperbolic geometry into the realm of single-cell data analysis.

  Language:HTML14631

• refresh-bio/kmer-db

  Kmer-db is a fast and memory-efficient tool for large-scale k-mer analyses (indexing, querying, estimating evolutionary relationships, etc.).

  Language:C++7516

• refresh-bio/fqsqueezer

  FQSqueezer - FASTQ compressor based on k-mer statistics

  Language:C++15

• StaPH-B/docker-builds

  :package: :whale: Dockerfiles and documentation on tools for public health bioinformatics

  Language:Dockerfile173115

• romanhaa/Cerebro

  Visualization of scRNA-seq data.

  Language:HTML9019

• ceegeeCode/GISA

  C code and more for the GISA algorithm

  Language:C5

• seasamgo/rescue

  Bootstrap imputation for scRNAseq data

  Language:R122

• volkamerlab/teachopencadd

  TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data

  Language:Jupyter Notebook707196

• must-bio-team/MVIAm

  Language:R11

• dpeerlab/Palantir

  Single cell trajectory detection

  Language:Jupyter Notebook20245

• mufeili/DL4MolecularGraph

  Literature of deep learning for graphs in Chemistry and Biology

  19548