Bioinformatics
Showing 36 of 146 projects
A Python library for molecular processing built on RDKit with a simple API and good defaults.
A C library for reading whole slide image files (virtual slides) with a consistent API across multiple vendor formats.
A curated reading list of foundational genomics papers for computational biologists and statistical genomics students.
A scalable Python toolkit for RNA velocity analysis in single cells using dynamical modeling.
A foundation model for multi-species genome understanding, achieving state-of-the-art performance on 28 genomic tasks.
An automated cell type annotation tool for single-cell RNA-seq data using logistic regression classifiers.
An R package to infer gene regulatory networks and identify cell types from single-cell RNA-seq data.
A deep learning toolkit for predicting regulatory activity, 3D genome folding, and mRNA half-life from DNA/RNA sequences.
A command-line toolkit for efficient querying and manipulation of NCBI Taxonomy data, with support for custom taxonomies.
A deep learning framework for integrating single-cell multi-omics data using graph-linked unified embeddings.
A dependency-free cross-platform Swiss Army knife for manipulating and editing Protein Data Bank (PDB) files.
An open-source toolkit for scalable, standardized computational pathology analysis, enabling AI and machine learning on large imaging datasets.
A curated list of resources for Biomedical Information Extraction (BioIE), including datasets, tools, libraries, and research.
A comprehensive benchmark suite for evaluating protein fitness prediction models using deep mutational scanning and clinical variant data.
A tool-augmented LLM that uses NCBI Web APIs to answer biomedical questions with high accuracy and reduced hallucinations.
A Python package for benchmarking and evaluating single-cell genomics data integration methods.
A 100M-parameter foundation model for single-cell transcriptomics, enabling gene expression enhancement, drug response prediction, and perturbation analysis.
An R package for creating interactive, cluster-based heatmaps using plotly for online publishing and data exploration.
A factor analysis framework for unsupervised integration of multi-omics datasets.
A geometric deep learning model that predicts transcriptional outcomes of single and multi-gene perturbations from single-cell RNA-seq data.
A BERT-based foundation model pretrained on large-scale scRNA-seq data for automated cell type annotation in single-cell analysis.
An integrative hetnet (heterogeneous network) encoding biomedical knowledge for drug repurposing and discovery.
A modular Python framework for exploratory analysis of heterogeneous epidemiological and electronic health record (EHR) data.
A collection of genomic language models for predicting variant effects and evolutionary constraints from DNA sequences.
A single-cell foundation model that uses ChatGPT embeddings from NCBI gene descriptions for gene-level and cell-level biology tasks.
A curated collection of research papers tracking the frontier of AI-based protein design methods and applications.
A large-scale scientific visualization platform for interactive ray-tracing of neurons and other biological data.
Deep learning model using convolutional neural networks to predict drug-target binding affinity from protein sequences and compound SMILES.
GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.
A zero-shot foundation model for generating universal embeddings from single-cell gene expression data.
Ankh is a state-of-the-art protein language model for general-purpose protein modeling and engineering tasks.
A deep convolutional neural network that predicts RNA-seq coverage at 32bp resolution from DNA sequence.
A curated collection of papers, datasets, tools, and resources for applying machine learning to small-molecule drug discovery.
A bi-directional equivariant transformer for long-range DNA sequence modeling, enabling reverse-complement aware genomic analysis.
An open and extensible Fiji plugin for single-particle tracking in life-science microscopy images.
A transformer-based model for predicting drug-target interactions using substructural pattern mining and augmented transformer encoders.
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