https://github.com/animesh/flashida
Intelligent Data Acquisition for Top-down proteomics on Thermo tribrid platform
https://github.com/animesh/foldseek
Foldseek enables fast and sensitive comparisons of large structure sets.
https://github.com/animesh/fixmatch
A simple method to perform semi-supervised learning with limited data.
https://github.com/animesh/fastp
An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)
https://github.com/animesh/explainn
ExplaiNN: interpretable and transparent neural networks for genomics
https://github.com/animesh/eukprot
Helper Perl scripts and tree files associated with the EukProt database of genome-scale eukaryotic protein sequence data sets.
https://github.com/animesh/embeddings
Code for AMIA CRI 2016 paper "Learning Low-Dimensional Representations of Medical Concepts"
https://github.com/animesh/easymicroplot
A easy R script plot for Microbiome analysis
https://github.com/animesh/domino
Network-based module discovery algorithm with high rate of empirically-validated term calls
https://github.com/animesh/dgl-ke
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
https://github.com/animesh/deepqmc
Deep learning quantum Monte Carlo for electrons in real space
https://github.com/animesh/deepcell-tf
Deep Learning Library for Single Cell Analysis
https://github.com/animesh/deepdia
Using deep learning to generate in silico spectral libraries for data-independent acquisition analysis. You can also use the online service powered by Omicsolution.
https://github.com/animesh/decoupler
R package to infer biological activities from omics data using a collection of methods.