🏃 The go-to single-cell Foundation Model
- batch-correction
- cell-biology
- celltype-annotation
- denoising
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🏃 The go-to single-cell Foundation Model
LLM for Drug Editing, ICLR 2024
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
Interpretable bilinear attention network with domain adaptation improves drug-target prediction.
Data visualisation web components for the life sciences.
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
Integrated pipeline for multiplexed image analysis
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
The repository host gives no description.
TypeDB Bio: Biomedical Knowledge Graph
NeuroAnatomy Toolbox: An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons.
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.
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
Predicting Cell Health with Morphological Profiles
3D shape analysis using deep learning
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
The repository host gives no description.
Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning
Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.