A dependency-free cross-platform swiss army knife for PDB files.
- python
- protein
- pdb
- bioinformatics
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A dependency-free cross-platform swiss army knife for PDB files.
Benchmarking analysis of data integration tools
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Reference mapping for single-cell genomics
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
UCE is a zero-shot foundation model for single-cell gene expression data
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Ankh: Optimized Protein Language Model
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
talk with NCBI entrez using R
scPerturb: A resource and a python/R tool for single-cell perturbation data
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
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
Open-source software for exploring and analyzing large, high-dimensional image-derived data.
OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data.
🏃 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
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