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
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
talk with NCBI entrez using R
A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data.
Probabilistic cell segmentation for in situ spatial transcriptomics
scPerturb: A resource and a python/R tool for single-cell perturbation data
The repository host gives no description.
Open-source software for exploring and analyzing large, high-dimensional image-derived data.
LLM for Drug Editing, ICLR 2024
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
A tool for cell instance aware segmentation in densely packed 3D volumetric images
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
Structural variant detection and association testing
Rails frontend to The Genome Institute's drug gene interaction database.
Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.
The framework for quantification of neuronal anatomy
Tool for precise, interactive, fast and scalable FISH spot detection
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
Predicting Cell Health with Morphological Profiles
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"
Data logistics system enabling real-time pathogen surveillance. Built for the Seattle Flu Study.
scPair: Boosting single cell multimodal analysis by leveraging single cell atlases and implicit feature selection