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GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Reference mapping for single-cell genomics
- single-cell
- deep-learning
- rna-seq-analysis
- data-integration
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
- dna
- language-model
- variant-effect-prediction
- genomics
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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
- bioinformatics
- exact-optimization
- metabolomics
- paired-data
RNA-seq prediction with deep convolutional neural networks.
Bi-Directional Equivariant Long-Range DNA Sequence Modeling
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
- cancer-genomics
- cnv-detection
- single-cell
- phylogeny
talk with NCBI entrez using R
A Network Integration Approach for Drug-Target Interaction Prediction
scPerturb: A resource and a python/R tool for single-cell perturbation data
- cite-seq
- database
- perturbation
- scatac-seq
Open source tumor evidence matching for sequencing labs and hospitals without molecular tumor boards. Research prototype; oncologist review required.
- bioinformatics
- cancer-research
- computational-biology
- drug-repurposing
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.
- cellprofiler-analyst
- cellprofiler
- image-analysis
- machine-learning
OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data.
🏃 The go-to single-cell Foundation Model
- batch-correction
- cell-biology
- celltype-annotation
- denoising
LLM for Drug Editing, ICLR 2024
- chatgpt
- conversation
- drug
- drug-discovery
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
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
- self-attention
- compound-protein-interaction
- transformercpi
- structure-free-virtual-sreening
Interpretable bilinear attention network with domain adaptation improves drug-target prediction.
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
- protein
- protein-sequences
- protein-design
- representation-learning