Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
- bioinformatics
- exact-optimization
- metabolomics
- paired-data
77,852 visible repositories in the index
25–48 of 62 · Most stars
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
Probabilistic cell segmentation for in situ spatial transcriptomics
A Network Integration Approach for Drug-Target Interaction Prediction
scPerturb: A resource and a python/R tool for single-cell perturbation data
Open source tumor evidence matching for sequencing labs and hospitals without molecular tumor boards. Research prototype; oncologist review required.
Repository for Nicheformer: a foundation model for single-cell and spatial omics
🏃 The go-to single-cell Foundation Model
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.
Accurate and efficient spot detection for microscopy data
The repository host gives no description.
The repository host gives no description.
A tool for cell instance aware segmentation in densely packed 3D volumetric images
Learning cell communication from spatial graphs of cells
code to run sei and obtain sei and sequence class predictions
Integrated pipeline for multiplexed image analysis
Rails frontend to The Genome Institute's drug gene interaction database.
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations