nicole1q

nicole1q's Stars

• RVC-Boss/GPT-SoVITS

  1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

  Language:Python37.7k2221.4k4.3k

• openai/CLIP

  CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

  Language:Jupyter Notebook26.7k3254053.4k

• microsoft/unilm

  Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

  Language:Python20.5k3071.4k2.6k

• peng-zhihui/Dummy-Robot

  我的超迷你机械臂机器人项目。

  Language:C12.6k3411772.8k

• yerfor/GeneFacePlusPlus

  GeneFace++: Generalized and Stable Real-Time 3D Talking Face Generation; Official Code

  Language:Python1.6k30220235

• bowang-lab/scGPT

  Language:Jupyter Notebook1.1k34239222

• dafish-ai/NTU-Machine-learning

  **大学李宏毅老师机器学习

  Language:Jupyter Notebook990300384

• jhpoelen/zenodo-upload

  upload big files to Zenodo using cURL, jq and bash

  Language:Shell24051239

• yiqunchen/GenePT

  Language:Jupyter Notebook21681531

• stemangiola/CuratedAtlasQueryR

  Tidy R query API for the harmonised and curated CELLxGENE single-cell atlas.

  Language:R895757

• ncborcherding/utility

  Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR

  Language:R83567

• tanlabcode/spatial-bonemarrow-atlas

  This repository contains the code used to generate figures and perform analysis for the manuscript titled "Transcriptomic and Spatial Proteomic Profiling Reveals the Cellular Composition and Spatial Organization of the Human Bone Marrow Microenvironment"

  Language:Jupyter Notebook690019

• jump-cellpainting/2024_Chandrasekaran_NatureMethods

  Language:Jupyter Notebook57102711

• SuperBianC/scMulan

  Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.

  Language:Jupyter Notebook552137

• SchubertLab/mvTCR

  Language:Jupyter Notebook484184

• wukevin/tcr-bert

  Large language modeling applied to T-cell receptor (TCR) sequences.

  Language:Jupyter Notebook481118

• cnio-bu/beyondcell

  Beyondcell is a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq and Spatial Transcriptomics data.

  Language:R461874

• ZJUFanLab/scRank

  A computational method to rank and infer drug-responsive cell population towards in-silico drug perturbation using a target-perturbed gene regulatory network (tpGRN) for single-cell transcriptomic data

  Language:R443125

• BFeng14/ActFound

  Official implementation for ActFound: A bioactivity foundation model using pairwise meta-learning

  Language:Python352122

• SBIlab/NetBio

  Language:Python34109

• cole-trapnell-lab/sci-plex

  repository containing analysis scripts and auxiliary files

  Language:R30742

• myzhengSIMM/TranSiGen

  Language:Jupyter Notebook234

• zangzelin/code_EVNet_DMTEV

  Language:Python11100

• azizilab/DIISCO_public

  Publically accessible code for DIISCO method

  Language:Jupyter Notebook9231

• aapupu/MIST

  An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

  Language:Jupyter Notebook7100

• ShalekLab/compressed_screening

  Code for the manuscript: Compressed phenotypic screening empowers scalable biological discovery.

  Language:Jupyter Notebook6601

• hliulab/cycleCDR

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

  Language:Python310

• broadinstitute/CellCap

  Interpret perturbation responses from scRNA-seq perturbation experiments

  Language:Python2423

• PangeaResearch/enlight-data

  Datasets to accompanied ENLIGHT manuscript

  2203

• stemangiola/infiltrating_immune_dataset

  Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR

  Language:R200