hebrian's Stars
run-llama/llama_index
LlamaIndex is a data framework for your LLM applications
khuyentran1401/Data-science
Collection of useful data science topics along with articles, videos, and code
khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists
Efficient Python Tricks and Tools for Data Scientists
ml-tooling/best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
reneshbedre/bioinfokit
Bioinformatics data analysis and visualization toolkit
MartinHeroux/spike2py
spike2py provides a simple interface to analyse and visualise data
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
kanaverse/kana
Single cell analysis in the browser
GangCaoLab/CoolBox
Jupyter notebook based genomic data visualization toolkit.
shervinea/mit-15-003-data-science-tools
Study guides for MIT's 15.003 Data Science Tools
crazyhottommy/scRNAseq-analysis-notes
scRNAseq analysis notes from Ming Tang
ajalt/fuckitpy
The Python error steamroller.
OpenGene/fastp
An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)
jts/bam2fastq
Simple convertor from bam to FASTQ
kevinjliang/Duke-Tsinghua-MLSS-2017
Duke-Tsinghua Machine Learning Summer School 2017
broadinstitute/CellBender
CellBender is a software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data.
foreshadow/atom-python-run
A simple atom package. Press one key to run your python code in atom.
databio/pararead
Simplifies parallel processing of DNA sequencing reads
jpreall/Clippings
Tool for annotating TSO-clipped reads from 10X Genomics scRNAseq data
hussius/deeplearning-biology
A list of deep learning implementations in biology
liuhuang31/BM3D-Denoise
Using BM3D to denoise
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data"