ChihyunPark / DNN_for_ADprediction
☆19Updated 4 years ago
Alternatives and similar repositories for DNN_for_ADprediction:
Users that are interested in DNN_for_ADprediction are comparing it to the libraries listed below
- DCAP:Integrating multi-omics data with deep learning for predicting cancer prognosis☆21Updated 4 years ago
- Python Implementation of SIMLR for single-cell visualization and analysis☆23Updated 2 years ago
- Large-scale automatic feature selection for biomarker discovery in high-dimensional OMICs data☆29Updated last year
- using shallow neural network layer (embedding) to infer gene-gene/sample relationship from gene expression data☆21Updated 6 years ago
- ☆65Updated last year
- Classifying tumor types based on Whole Genome Sequencing (WGS) data☆48Updated last year
- Generative adversarial networks for single-cell RNA-seq imputation☆39Updated 4 years ago
- repository with the scripts to run examples of the publications with new functionalities of TCGAbiolinks☆15Updated 3 years ago
- Python framework for single-cell RNA-seq clustering with special focus on transfer learning.☆18Updated 5 years ago
- Graph Neural Network model for survival analysis from multi-omics data☆12Updated 3 years ago
- Python 2.7 implementation of network-based stratification (NBS) algorithm from Hofree et al (Nature Methods 2013)☆37Updated 6 months ago
- ☆54Updated 2 years ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆49Updated 3 years ago
- Multi-omics integration method using AE and GCN☆34Updated last year
- ☆20Updated 3 years ago
- Deep-Learning framework for multi-omic and survival data integration☆82Updated last year
- ☆49Updated 5 years ago
- resVAE is a restricted latent variational autoencoder that we wrote to uncover hidden structures in gene expression data, especially usin…☆12Updated 2 years ago
- covolutional neural network based coexpression analysis☆76Updated 4 years ago
- Code to reproduce results in Ravindra NG* & Sehanobish A* et al. 2020, "Disease State Prediction From Single-Cell Data Using Graph Attent…☆16Updated 4 years ago
- BERMUDA (Batch Effect ReMoval Using Deep Autoencoders) is a novel transfer-learning-based method for batch correction in scRNA-seq data.☆30Updated 5 years ago
- jupyter notebook; perform differential gene expression analysis using DESeq2 on TCGA RNAseq data☆32Updated 6 years ago
- Building classifiers using cancer transcriptomes across 33 different cancer-types☆119Updated 5 years ago
- Similarity Network Fusion☆32Updated 9 years ago
- An unsupervised scRNA-seq analysis workflow with graph attention networks☆24Updated last year
- Deep neural networks for predicting CpG methylation☆37Updated 8 years ago
- scPretrain: Multi-task self-supervised learning for cell type classification☆23Updated 3 years ago
- "DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications", Bioinformatics, Volume 34, Issue 17,☆20Updated 5 years ago
- using graph convolutional neural network and spaital transcriptomics data to infer cell-cell interactions☆56Updated 3 years ago
- ☆19Updated 4 years ago