Repository Index

Apache KIE (Knowledge Is Everything) is the home of Drools, OptaPlanner, jBPM, and Kogito.

• rule-engine
• java
• dmn
• hacktoberfest

A Cloud Native Batch System (Project under CNCF)

• batch-systems
• kubernetes
• golang
• hpc

An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

Fast and flexible physics-based battery models in Python

• pybamm
• battery-models
• solvers
• python

Cloud-native genomic dataframes and batch computing

• genetics
• vcf
• genomics
• gwas

:triangular_ruler: Numerical integration (quadrature, cubature) in Python

• quadrature
• integration
• mathematics
• engineering

GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations

Reference mapping for single-cell genomics

• single-cell
• deep-learning
• rna-seq-analysis
• data-integration

Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome

• cancer-genomics
• cnv-detection
• single-cell
• phylogeny

scPerturb: A resource and a python/R tool for single-cell perturbation data

• cite-seq
• database
• perturbation
• scatac-seq

🏃 The go-to single-cell Foundation Model

• batch-correction
• cell-biology
• celltype-annotation
• denoising

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

• disentanglement
• drug-discovery
• genomics
• perturbation

Delphi matrix library

PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.

Variations on Kirby & Paris' hydra battles and other entertaining math in Coq (collaborative, documented, includes exercises) [maintainer=@Casteran]

• coq
• hydra-battles
• ordinal-notations
• formal-proofs

High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning