ceesu

ceesu's Stars

• oven-sh/bun

  Incredibly fast JavaScript runtime, bundler, test runner, and package manager – all in one

  Language:Zig72.9k6078.5k2.6k

• stitionai/devika

  Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika aims to be a competitive open-source alternative to Devin by Cognition AI.

  Language:Python18.2k2033832.4k

• organicmaps/organicmaps

  🍃 Organic Maps is a free Android & iOS offline maps app for travelers, tourists, hikers, and cyclists. It uses crowd-sourced OpenStreetMap data and is developed with love by MapsWithMe (MapsMe) founders and our community. No ads, no tracking, no data collection, no crapware. Please donate to support the development!

  Language:C++9.5k894.7k913

• rawgraphs/rawgraphs-app

  A web interface to create custom vector-based visualizations on top of RAWGraphs core

  Language:JavaScript8.6k3233011.8k

• peak/s5cmd

  Parallel S3 and local filesystem execution tool.

  Language:Go2.6k31367223

• AllenDowney/ThinkBayes2

  Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

  Language:Jupyter Notebook1.8k69491.5k

• rmcelreath/stat_rethinking_2024

  Language:R86554299

• lrberge/fixest

  Fixed-effects estimations

  Language:R371848359

• paws-r/paws

  Paws, a package for Amazon Web Services in R

  Language:R313927037

• bioSyntax/bioSyntax

  Syntax highlighting for computational biology

  Language:Shell252174930

• biomap-research/scFoundation

  Language:Jupyter Notebook22185134

• nigelpoulton/gsd

  Files for Getting Started with Docker video training course

  Language:CSS200135625

• snap-stanford/GEARS

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

  Language:Python18967540

• aertslab/scenicplus

  SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data.

  Language:Jupyter Notebook1751029828

• HelenaLC/muscat

  Multi-sample multi-group scRNA-seq analysis tools

  Language:R1581412232

• lynnlangit/aws-for-bioinformatics

  AWS for Bioinformatics Researchers

  Language:Jupyter Notebook1147028

• cmudig/emblaze

  Interactive Jupyter notebook widget for visually comparing embedding spaces.

  Language:Svelte100335

• theislab/chemCPA

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

  Language:Jupyter Notebook9659823

• theislab/cpa

  The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

  Language:Python8054418

• const-ae/lemur

  Latent Embedding Multivariate Regression

  Language:R785167

• coolbutuseless/svgparser

  Render SVG images in R. Load SVG data as data.frames

  Language:R46481

• thomasmaxwellnorman/Perturbseq_GI

  Code for reproducing "Exploring genetic interaction manifolds constructed from rich single-cell phenotypes"

  Language:Jupyter Notebook441220

• drisso/zinbwave

  Clone of the Bioconductor repository for the zinbwave package, see https://bioconductor.org/packages/zinbwave

  Language:R4386610

• microsoft/zero-shot-scfoundation

  Language:Jupyter Notebook427512

• Genentech/iterative-perturb-seq

  Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors

  Language:Jupyter Notebook31512

• douglasyao/FR-Perturb

  Factorize-Recover for Perturb-seq analysis (FR-Perturb)

  Language:Python17152

• yulun-rayn/graphVCI

  This repository implements Graph Variational Causal Inference (graphVCI), a framework that integrates prior knowledge of relational information into variational causal inference for the prediction of perturbation effect on gene expressions at single-cell and marginal level.

  Language:Python15233

• powellgenomicslab/BGI_vs_Illumina_Benchmark

  This repository contains code related to the publication "Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing" by Senabouth et al.

  Language:Shell14318

• EvaYiwenWang/Managing_batch_effects

  Language:R137

• wvictor14/rbiotechsalary

  reddit r/biotech salary

  Language:R1110