MOGONET
Multi-omics classifier
A framework for multi-omics data integration and classification using graph convolutional networks
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
144 stars
3 watching
50 forks
Language: Python
last commit: over 5 years agoLinked from 1 awesome list
mikelove/awesome-multi-omicsRelated projects:
| Repository | Description | Stars |
|---|---|---|
 subho406/omninet | An implementation of a unified architecture for multi-modal multi-task learning using PyTorch. | 515 |
 scverse/muon | A Python framework for multimodal omics analysis of high-throughput biological data | 222 |
 mwydmuch/napkinxc | A fast and simple library for multi-class and multi-label classification | 65 |
 bruinxiong/senet.mxnet | An implementation of Squeeze-and-Excitation Networks in MXNet for image classification tasks. | 155 |
 mustafaturan/omnicat | A framework providing a generalized strategy holder for text classification | 11 |
 zhangxiaoyu11/omivae | This project provides an end-to-end deep learning model for low dimensional latent space extraction and multi-class classification on multi-omics datasets. | 31 |
 peterzzq/scdart | A deep learning framework designed to integrate multi-modal single cell data from different modalities while preserving cell trajectory structures. | 10 |
| mustafaturan/omnicat-bayes | An implementation of the Naive Bayes algorithm as a text classification strategy in Ruby. | 32 |
 zudi-lin/pytorch_connectomics | A deep learning framework for automatic and semi-automatic segmentation of 3D image stacks in connectomics | 172 |
 muhanzhang/dgcnn | A deep learning architecture for graph classification that extracts vertex features through propagation-based graph convolution and retains more node information than traditional sum pooling methods. | 174 |
 matthewhammer/motoko-graph | Provides a generic graph data model and query algorithms for modeling various types of relationships and dependencies. | 17 |
 yangnianzu0515/moleood | An implementation of a molecular representation learning method with substructure invariance for out-of-distribution generalization. | 61 |
 mg2033/shufflenet | An implementation of a computationally efficient deep neural network architecture designed for mobile devices with limited computing power. | 383 |
 jhu99/scbean | Analyzes single-cell multi-omics data from various modalities like RNA-seq and ATAC-seq | 16 |
 juntang-zhuang/laddernet | A deep learning implementation of a multi-path network architecture for medical image segmentation | 140 |