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gcn-dnn

gcnn-survey-paper

ggnn

Gated Graph Sequence Neural Networks

ggnn.pytorch

A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN) and Residual Gated Graph ConvNets (RGGC) for FYP

github_test1

gnn-comparison

Official Repository of "A Fair Comparison of Graph Neural Networks for Graph Classification", ICLR 2020

gnnpapers

Must-read papers on graph neural networks (GNN)

graph2vec

Fastest python node2vec embeddings in the west

graphdrp

graphdta

GraphDTA: Predicting drug-target binding affinity with graph neural networks

graphembedding

Implementation and experiments of graph embedding algorithms.deep walk,LINE(Large-scale Information Network Embedding),node2vec,SDNE(Structural Deep Network Embedding),struc2vec

graphsage

Representation learning on large graphs using stochastic graph convolutions.

graphvite

GraphVite: A General and High-performance Graph Embedding System

hierarchical-attention-network

Implementation of Hierarchical Attention Networks in PyTorch

hin2vec

Heterogeneous information network representation learning

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.

ilearnplus

iLearnPlus is the first machine-learning platform with both graphical- and web-based user interface that enables the construction of automated machine-learning pipelines for computational analysis and predictions using nucleic acid and protein sequences.

kernalized-tsne

kernalized t-Distributed Stochastic Neighbor Embedding (t-SNE)

knowledgegraphcourse

东南大学《知识图谱》研究生课程

largevis

lihang-code

《统计学习方法》的代码实现

line

LINE: Large-scale information network embedding

machinelearning

My blogs and code for machine learning. http://cnblogs.com/pinard

mathfeature

Feature Extraction Package for Biological Sequences

mieaa-api

API and package code for miEAA 2.0

mifs

Parallelized Mutual Information based Feature Selection module.

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

models

Model zoo for genomics

multicore-tsne

Parallel t-SNE implementation with Python and Torch wrappers.

multirm