nicole1q

nicole1q's Stars

• mohuangrui/ucasthesis

  LaTeX Thesis Template for the University of Chinese Academy of Sciences

  Language:TeX3.6k65434943

• FreedomIntelligence/HuatuoGPT

  HuatuoGPT, Towards Taming Language Models To Be a Doctor. (An Open Medical GPT)

  Language:Python1.2k2252155

• mohuangrui/ucasproposal

  LaTeX Proposal Template for the University of Chinese Academy of Sciences

  Language:TeX646920146

• jerryji1993/DNABERT

  DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

  Language:Python64412115167

• vqdang/hover_net

  Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.

  Language:Python57411262238

• aristoteleo/dynamo-release

  Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses

  Language:Python445926460

• morris-lab/CellOracle

  This is the alpha version of the CellOracle package

  Language:Python343722060

• IBM/molformer

  Repository for MolFormer

  Language:Jupyter Notebook297102048

• jump-cellpainting/datasets

  Images and other data from the JUMP Cell Painting Consortium

  Language:Shell167118118

• hachmannlab/chemml

  ChemML is a machine learning and informatics program suite for the chemical and materials sciences.

  Language:Python164121132

• 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:Python9744616

• broadinstitute/cellpainting-gallery

  Cell Painting Gallery

  8164513

• nitzanlab/biolord

  Language:Python773105

• OSU-BMBL/deepmaps

  Single-cell biological network inference using a heterogeneous graph transformer

  Language:HTML71476

• kekegg/DLEPS

  A Deep Learning based Efficacy Prediction System for drug discovery

  Language:Jupyter Notebook6581335

• phenixace/MolReGPT

  Language:Python49222

• pth1993/DeepCE

  A deep learning framework for high-throughput mechanism-driven phenotype screening

  Language:Python491521

• uta-smile/SMILES-BERT

  Language:Python456313

• SUwonglab/PECA

  PECA is a software for inferring context specific gene regulatory network from paired gene expression and chromatin accessibility data

  Language:MATLAB434147

• lanagarmire/Asgard

  Language:R4121420

• welch-lab/PerturbNet

  PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation

  Language:Jupyter Notebook302410

• bowang-lab/scFormer

  Language:Python195114

• carpenter-singh-lab/2023_Cimini_NatureProtocols

  Optimizing the Cell Painting assay for image-based profiling

  Language:Jupyter Notebook193122

• jianglikun/DeepTTC

  DeepTTC: a transformer-based model for predicting cancer drug response

  Language:Python152310

• lanagarmire/Single-cell-drug-repositioning

  Language:R15267

• MaayanLab/L1000toRNAseq

  Language:Jupyter Notebook1444

• theislab/multicpa

  Language:Python14212

• CaicedoLab/2023_Moshkov_NatComm

  Predicting assays Using Morphological Analysis

  Language:Jupyter Notebook13526

• theislab/IMPA

  Language:Jupyter Notebook12231

• nishanth83/NSCLC

  Language:R2