hfukuse

hfukuse's Stars

• SYSTRAN/faster-whisper

  Faster Whisper transcription with CTranslate2

  Language:Python13.3k1257781.1k

• seandavi/awesome-single-cell

  Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

  3.3k24923993

• tanghaibao/goatools

  Python library to handle Gene Ontology (GO) terms

  Language:Python79623214209

• OmicsML/awesome-deep-learning-single-cell-papers

  71131592

• chanzuckerberg/cellxgene

  An interactive explorer for single-cell transcriptomics data

  Language:JavaScript653341.4k125

• milaboratory/mixcr

  MiXCR is an ultimate software platform for analysis of Next-Generation Sequencing (NGS) data for immune profiling.

  Language:Kotlin3383198779

• Sachini/ronin

  RoNIN: Robust Neural Inertial Navigation in the Wild

  Language:Python3252051110

• kaityo256/python_zero

  ゼロから学ぶPython

  Language:HTML298134435

• liulab-dfci/TRUST4

  TCR and BCR assembly from RNA-seq data

  Language:C292629750

• TencentAILabHealthcare/scBERT

  Language:Python28956863

• snap-stanford/GEARS

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

  Language:Python22068242

• crazyhottommy/TCR-BCR-seq-analysis

  T/B cell receptor sequencing analysis notes

  21510032

• JackieHanLab/TOSICA

  Transformer for One-Stop Interpretable Cell-type Annotation

  Language:Jupyter Notebook13422026

• saeyslab/multinichenetr

  MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

  Language:HTML130310314

• bunnech/cellot

  Learning Single-Cell Perturbation Responses using Neural Optimal Transport

  Language:Python12334213

• tubo213/kaggle-child-mind-institute-detect-sleep-states

  Language:Jupyter Notebook11842484

• jzhoulab/puffin

  deep learning-inspired explainable sequence model for transcription initiation

  Language:Python921118

• MadhavShashi/Human-Activity-Recognition-Using-Smartphones-Sensor-DataSet

  Human activity recognition, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model.

  Language:Jupyter Notebook711024

• discus0434/paper-summarizer

  A Slack Bot for summarizing arXiv papers, powered by OpenAI LLMs.

  Language:Python68203

• markub3327/HAR-Transformer

  Transformer for Human Activity Recognition

  Language:Jupyter Notebook653716

• favilaco/deconv_benchmark

  Language:R585620

• ecker-lab/sensorium_2023

  repo for the sensorium 2023 competition

  Language:Jupyter Notebook252913

• terminalai/GaitMonitoringForParkinsonsDiseasePatients

  To monitor gait patterns to detect freezing of gait. Done as part of the Singapore Science Mentorship Programme + CS4131 module + extended.

  Language:Jupyter Notebook18117

• welch-lab/MichiGAN

  Learning disentangled representations of single-cell data for high-quality generation

  Language:Python15324

• Jean-KOUAGOU/1st-place-solution-single-cell-pbs

  This repository implements the winning solution for the single cell perturbation problems

  Language:Python14208

• KANG-BIOINFO/CellDrift

  CellDrift: temporal perturbation effects for single cell data

  Language:Python13111

• Suryanarayanan-Balaji/GPT-MolBERTa

  Language:Python12102

• TeamAidemy/ds-paper-summaries

  株式会社アイデミーのデータサイエンティストによる論文サマリー

  116570

• chumajin/kaggle-commonlit

  Language:Python7101

• mukamel-lab/robustlink

  A python package that links enhancers to genes using single-cell transcriptomes and epigenomes

  Language:Python3000