liuchuan111

alphafold

Open source code for AlphaFold.

timeOmics

Time-Course Multi-Omics data integration

iFeatureOmega-CLI

iFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, iFeatureOmega supplies the largest number of feature extraction and analysis approaches for most molecule types compared to other pipelines. Three versions (i.e. iFeatureOmega-Web, iFeatureOmega-GUI and iFeatureOmega-CLI) of iFeatureOmega have been made available to cater to both experienced bioinformaticians and biologists with limited programming expertise. iFeatureOmega also expands its functionality by integrating 15 feature analysis algorithms (including ten cluster algorithms, three dimensionality reduction algorithms and two feature normalization algorithms) and providing nine types of interactive plots for statistical features visualization (including histogram, kernel density plot, heatmap, boxplot, line chart, scatter plot, circular plot, protein three dimensional structure plot and ligand structure plot). iFeatureOmega is an open-source platform for academic purposes. The web server can be accessed through http://ifeature2.erc.monash.edu and the GUI and CLI versions can be download at: https://github.com/Superzchen/iFeatureOmega-GUI and https://github.com/Superzchen/iFeatureOmega-CLI, respectively.

TO-GCN

Pipeline of time-ordered gene coexpression network (TO-GCN) construction from three-dimensional (gene expression, condition, and time) data

rnaseq_tutorial

Informatics for RNA-seq: A web resource for analysis on the cloud. Educational tutorials and working pipelines for RNA-seq analysis including an introduction to: cloud computing, critical file formats, reference genomes, gene annotation, expression, differential expression, alternative splicing, data visualization, and interpretation.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

eoffice

Export and import graphics and tables to MicroSoft office

MSK-IMPACT-IO

DPPI

A convolutional neural network to predict PPI interactions

auto_xuexiqiangguo

学习强国 每日拿满61分！免root 四人赛双人对战秒答 自动化脚本

benchmark_eval

Python-100-Days

Python - 100天从新手到大师

StackPPI

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

dryR

Differential RhythmicitY analysis in R is an R package that provides the statistical framework to assess differential rhythmicity of a time series with two and more conditions.

BingWallpaper

收集Bing必应每日壁纸