Schema and generated objects for biolink data model and upper ontology
- biolink
- monarchinitiative
- gene-ontology
- datamodel
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Schema and generated objects for biolink data model and upper ontology
Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Ankh: Optimized Protein Language Model
Repository for Nicheformer: a foundation model for single-cell and spatial omics
OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data.
🏃 The go-to single-cell Foundation Model
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
Learning cell communication from spatial graphs of cells
code to run sei and obtain sei and sequence class predictions
Official repo for CellPLM: Pre-training of Cell Language Model Beyond Single Cells.
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
BulkFormer: A large-scale foundation model for human bulk transcriptomes
PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.
Run scripts, explore your genome, and get personalized explanations — all in natural dialogue.
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
MCP server for spatial transcriptomics analysis through natural language interfaces.
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders
Official implementation of JAMIE, Joint variational Autoencoders for Multimodal Imputation and Embedding
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.