RasmussenLab / MOVELinks
MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
☆79Updated 7 months ago
Alternatives and similar repositories for MOVE
Users that are interested in MOVE are comparing it to the libraries listed below
Sorting:
- Multi-omics integration method using AE and GCN☆35Updated 2 years ago
- ☆66Updated last year
- Models and datasets for perturbational single-cell omics☆152Updated 2 years ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆50Updated 3 years ago
- ☆43Updated last year
- High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations☆48Updated 4 months ago
- ☆31Updated 2 years ago
- Utilizing single-cell omics from patients tumor to predict response and resistance.☆69Updated 2 years ago
- scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics☆97Updated 3 months ago
- ☆55Updated 2 years ago
- ☆90Updated 4 years ago
- Compendium of available lists of ligand-receptor pairs and surface-secreted protein interactions.☆132Updated 2 years ago
- Create cell sentences from sequencing data☆22Updated 10 months ago
- Integrate Any Omics: Towards genome-wide data integration for patient stratification☆55Updated last month
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆128Updated 3 months ago
- ☆107Updated 3 weeks ago
- Deep Learning the T Cell Receptor Binding Specificity of Neoantigen☆80Updated 3 years ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆100Updated 10 months ago
- A simulator for single cell multi-omics and spatial omics data that provides ground truth to benchmark a wide range of methods.☆52Updated 5 months ago
- Code for paper "A deep profile of gene expression across 18 human cancers"☆25Updated 6 months ago
- ☆15Updated last year
- Codes for paper: Evaluating the Utilities of Large Language Models in Single-cell Data Analysis.☆70Updated last week
- Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence☆39Updated 2 years ago
- KGWAS: novel genetics discovery enabled by massive functional genomics knowledge graph☆69Updated 3 months ago
- Additional code and analysis from the single-cell integration benchmarking project☆67Updated 2 years ago
- ☆56Updated last week
- Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data☆119Updated 2 weeks ago
- Transformer for One-Stop Interpretable Cell-type Annotation☆142Updated last year
- Deep Transfer Learning of Drug Sensitivity by Integrating Bulk and Single-cell RNA-seq data☆54Updated 11 months ago
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆34Updated last month