aweGNN with medical data science

packages & environment(most used)

• python
  1. tensorflow 1/2
  2. PyTorch 1.5.0
  3. networkX
• R
  1. igraph (graph data ,visualisation)
  2. scapGNN (Active Pathway and Gene Module Inference from Single-cell Multi-omics Data)
  3. scGNNLTMG BMEngineeR/scGNNLTMG: LTMG for scGNN (github.com)
  4. cancerID_GNN chopper6/cancerID_GNN: Biointelligence project: a graph neural network identifies cancer subtypes (github.com)
  5. Graph_PlusMinus conormalone/Graph_PlusMinus: GNN based plus minus from NBA PBP Data (github.com)
  6. spaGCN package in R JianingYao/spaGCN_R: spaGCN package in R (github.com)
  7. Flexible Protocol for Targeted Gene Co-expression Network Analysis dcomanschmid/tGCN: Flexible Protocol for Targeted Gene Co-expression Network Analysis (github.com)

     medical CV

  8. 3dCT 分类 iMED-Lab/UG-GAT (github.com)

     medical Gene-gene/RNA/protein/metabolite interaction

  9. A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data yunchuankong/GEDFN: GEDFN: Graph-Embedded Deep Feedforward Network (github.com)
  10. RESEPT is a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics.OSU-BMBL/RESEPT: A deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics (github.com)
  11. Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network jianhuupenn/SpaGCN: SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network (github.com)
  12. scFEA: A graph neural network model to estimate cell-wise metabolic using single cell RNA-seq data changwn/scFEA: single cell Flux Estimation Analysis (scFEA) Try the below web server! (github.com)
  13. G-EDNN 数据处理important2017100647/G-EDNN: Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions (github.com)
  14. NIHGCN:Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions weiba/NIHGCN: Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions (github.com)
  15. Prediction of gene co-expression from chromatin contacts with graph attention networkPage not found · GitHub
  16. wxbCaterpillar/GE-Impute (github.com)
  17. A novel biomarker selection method combining graph neural network and gene relationships applied to microarray dataxwdshiwo/BioFSDatasets_and_code: code and datasets of ILRC (github.com)
  18. Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks biomed-AI/Hist2ST (github.com)
  19. Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle gywns6287/gmcNet: gene module clustering network (github.com)
  20.  GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expressionVictoria BOURGEAIS / GraphGONet · GitLab (univ-evry.fr)

  21. Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks https://github.com/jonathan-f/DA_RNN_GENEXP

  22. Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network DHUDBlab/scDSC (github.com)
  23. Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis github.com
  24. 【important】Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network weiba/ERGCN at master (github.com)
  25. Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases paulmorio/gincco: Source code for GINCCo Paper (github.com)
  26. 【data】Distant metastasis identification based on optimized graph representation of gene interaction patterns RanSuLab/Metastasis-glmGCN (github.com)
  27. Detecting Spatially Co-expressed Gene Clusters with Functional Coherence by Graph-regularized Convolutional Neural Network kuanglab/CNN-PReg (github.com)
  28. A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networksRaminHasibi/GraphFeatureAutoencoder: A repo for implementation of Graph features autoencoder for expression values prediction and imputation (github.com)
  29. A graph convolutional neural network for gene expression data analysis with multiple gene networks
  30. Improving cancer driver gene identification using multi-task learning on graph convolutional network weiba/MTGCN (github.com)