Jianbo1999

Jianbo1999's Stars

• AndersonHu85/ccRCC_multiomics

  Language:R138

• CSB5/CaDRReS-Sc

  CaDRReS-Sc is a framework for analyzing drug response heterogeneity based on single-cell RNA-seq data

  Language:Jupyter Notebook124

• kimmo1019/DeepCDR

  Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

  Language:Python6926

• myzhengSIMM/TranSiGen

  Language:Jupyter Notebook123

• ChanLab-UTSW/BreastCancer_Integrated

  Language:R174

• JackieHanLab/SenCID

  Senescent Cell Identification

  Language:Jupyter Notebook251

• ruppinlab/PERCEPTION

  Utilizing single-cell omics from patients tumor to predict response and resistance.

  5915

• ailabstw/scDrug

  scDrug: From scRNA-seq to Drug Repositioning

  Language:Python284

• kekegg/DLEPS

  A Deep Learning based Efficacy Prediction System for drug discovery

  Language:Jupyter Notebook6033

• snehamitra/SCARlink

  Language:Jupyter Notebook348

• UPSUTER/GEMLI

  Language:R255

• azizilab/starfysh

  Spatial Transcriptomic Analysis using Reference-Free auxiliarY deep generative modeling and Shared Histology

  Language:Jupyter Notebook9312

• Asa12138/ReporterScore

  Generalized Reporter Score-based Functional Enrichment Method for Omics Data

  Language:R453

• MannLabs/alphabase

  Infrastructure of AlphaX ecosystem

  Language:Jupyter Notebook288

• cdesterke/ferroviz

  R-package to perform visualization of ferroptosis related genes in a transcriptome differential expressed gene analysis output from limma algorithm

  Language:R1

• JackieHanLab/TOSICA

  Transformer for One-Stop Interpretable Cell-type Annotation

  Language:Jupyter Notebook12624

• bischofp/single_cell_lung_adenocarcinoma

  Language:Jupyter Notebook2911

• sqjin/CellChat

  R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data

  Language:R621142

• richardslab/metabolomics_GWAS_CLSA

  Language:R132

• zerland/vargen

  VarGen is an R package designed to get a list of variants related to a disease. It just need an OMIM morbid ID as input and optionally a list of tissues / gwas traits of interest to complete the results. You can also use your own customised list of genes. VarGen is capable of annotating the variants to help you identify the most impactful ones.

  1

• cclab-brca/neoadjuvant-therapy-response-predictor

  Language:R4727

• zhanghao-njmu/SCP

  An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.

  Language:R35781

• tiroshlab/3ca

  Code for reproducing the analysis in Gavish et al. "The transcriptional hallmarks of intra-tumor heterogeneity across a thousand tumors".

  Language:R708

• FredHutch/Galeano-Nino-Bullman-Intratumoral-Microbiota_2022

  Analysis code used in Galeano Nino et al., Impact of Intratumoral Microbiota on Spatial and Cellular Heterogeneity in human cancer. 2022

  Language:Python3210

• TangFei965/pan-NK

  These are scripts for pan-cacner NK cells analyses.

  Language:R5712

• hrcnlab/escc_pipline

  Language:R1

• perslab/CELLEX

  CELLEX (CELL-type EXpression-specificity)

  Language:Jupyter Notebook369

• perslab/CELLECT

  CELLECT (CELL-type Expression-specific integration for Complex Traits)

  Language:Python6819

• aerickso/SpatialInferCNV

  Clone Calling from Visium Spatial Transcriptomics of Cancer samples

  Language:HTML5513

• Zaoqu-Liu/IRLS

  Machine learning-based integrative analysis develops an immune-derived lncRNA signature for improving clinical outcomes in colorectal cancer

  Language:R9742