klai001

klai001's Stars

• Asabeneh/30-Days-Of-Python

  30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

  Language:Python43k8201528.2k

• smorabit/hdWGCNA

  High dimensional weighted gene co-expression network analysis

  Language:R363532436

• grst/rstudio-server-conda

  Run Rstudio Server in a conda environment

  Language:Shell23462749

• enblacar/SCpubr

  Generate high quality, publication ready visualizations for single cell transcriptomics data.

  Language:R15867513

• BlackHC/neural_net_checklist

  Language:Python139108

• rezacsedu/Deep-Learning-for-Clustering-in-Bioinformatics

  Deep Learning-based Clustering Approaches for Bioinformatics

  Language:Jupyter Notebook1345234

• rezacsedu/XAI-for-bioinformatics

  Explainable AI for Bioinformatics

  Language:Jupyter Notebook1174018

• jackbibby1/SCPA

  R package for pathway analysis in scRNA-seq data

  Language:R632656

• antndlcrx/oxford-llms-workshop

  Workshop on Learning and Applying Large Language Models for Social Science Research

  Language:Jupyter Notebook59108

• quadbio/scMultiome_analysis_vignette

  Tutorial of single-cell RNA-ATAC multiomic sequencing data analysis

  472613

• cellgeni/STARsolo

  wrapper scripts for convenient STARsolo processing of 10X and other scRNA-seq

  Language:Shell445125

• gdagstn/borges

  Antique or modern atlas-like depictions of non-linear dimensionality reduction for single cell data

  Language:R18120

• WojtekFulmyk/mlcausality

  Nonlinear Granger causality using machine learning techniques

  Language:Python162

• PhilBoileau/scPCA

  A toolbox for sparse contrastive principal component analysis

  Language:R124251

• uhlerlab/covid19_repurposing

  Language:Jupyter Notebook12404

• anant-dadu/PDProgressionSubtypes

  Identification and prediction of Parkinson disease subtypes and progression using machine learning in two cohorts

  Language:Jupyter Notebook11224

• cole-trapnell-lab/pseudospace

  Language:R112414

• kpatel427/multiome_analysis

  Scripts for sincle cell multiome analysis

  Language:R11318

• labsyspharm/DRIADrc

  Resources for Drug Repurposing In Alzheimer's Disease (DRIAD) work

  Language:R11438

• kdyson/R_Scripts

  Helper R scripts for multiple PERMANOVA tests, AICc script for PERMANOVA, etc.

  Language:R10303

• labsyspharm/DRIAD

  Drug Repurposing in Alzheimer's Disease

  Language:R8325

• bigomics/metaLINCS

  Language:R7101

• lanagarmire/COVID19-Drugs-LungInjury

  Language:R7201

• daifengwanglab/BOMA

  Brain and Organoid Manifold Alignment

  Language:R6101

• caleblareau/indRop

  An R package for basic indrop analyses and data handling

  Language:R5202

• MinYan19940/DynamicGRNPipe

  Language:R411

• xtsvm/CancerSubtypes

  Cancer subtypes identification, validation and visualization based on genomic data

  Language:HTML4314

• GibsonLab-GT/scMultiome-TFBS-Analysis

  The objective of this pipeline is to identify putative TF regulatory mechanisms focused on differentially accessible genes and differentially accessible chromatin obtained from single cell multiome analysis, with scRNA-seq and scATAC-seq data.

  Language:Shell200

• zpliulab/GreyNet

  Language:Python111