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bootmrmr icon bootmrmr

:exclamation: This is a read-only mirror of the CRAN R package repository. BootMRMR — Bootstrap-MRMR Technique for Informative Gene Selection

bootstrap icon bootstrap

The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.

ccaalgorithm icon ccaalgorithm

B. Baur and S. Bozdag, "A Canonical Correlation Analysis-Based Dynamic Bayesian Network Prior to Infer Gene Regulatory Networks from Multiple Types of Biological Data", Journal of Computational Biology, 2015.

dbn icon dbn

Create a dynamic Bayesian networks with constrained base algorithms

dcor icon dcor

Compute distance correlation

deeptraffic icon deeptraffic

Deep Learning models for network traffic classification

gnnpapers icon gnnpapers

Must-read papers on graph neural networks (GNN)

graph2seq icon graph2seq

Graph2Seq is a simple code for building a graph-encoder and sequence-decoder for NLP and other AI/ML/DL tasks.

hine icon hine

Heterogeneous Information Network Embedding

hmdbn icon hmdbn

Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

marktext icon marktext

📝A simple and elegant markdown editor, available for Linux, macOS and Windows.

mrmr icon mrmr

minimum Redundancy Maximum Relevance

nndl.github.io icon nndl.github.io

《神经网络与深度学习》 Neural Network and Deep Learning

oh-my-zsh icon oh-my-zsh

A delightful community-driven (with 1,000+ contributors) framework for managing your zsh configuration. Includes 200+ optional plugins (rails, git, OSX, hub, capistrano, brew, ant, php, python, etc), over 140 themes to spice up your morning, and an auto-update tool so that makes it easy to keep up with the latest updates from the community.

openne icon openne

An Open-Source Package for Network Embedding (NE)

protobuf icon protobuf

Protocol Buffers - Google's data interchange format

robotic-surgery icon robotic-surgery

This Java project is for the project "Robot Safety". In practice, this project is working with Feng Cao's Dynamic Bayesian Network model and Mark Renfrew's SABiR robot controller.

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