Find open-source science resources
A directory of tools, AI models, datasets, and research resources for biotech, bioinformatics, and other scientific fields. Aggregated from curated GitHub awesome-lists, HuggingFace, bio.tools, Bioconductor, and more.
Filters
Health
Domain
Language
License
Source(1)
Type
2,356 of 7,083 resources
Showing 2,351–2,356
Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays.
This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.
Tools for compositional and other sample-level ecological analyses and visualizations tailored for single-cell RNA-seq data. SETA includes functions for taxonomizing celltypes, normalizing data, performing statistical tests, and visualizing results. Several tutorials are included to guide users and introduce them to key concepts. SETA is meant to teach users about statistical concepts underlying ecological analysis methods so they can apply them to their own single-cell data.
Membrane Protein-Lipid Interaction Database. A large-scale experimentally validated dataset of 80685 residue-level lipid contact annotations across 4712 membrane proteins derived from PDB crystal and cryo-EM structures. Provides pre-computed binary contact labels, continuous distance values, sequence-identity-based cluster assignments, and ready-made train-validation-test splits for machine learning.
Plant Compound Extractor is a desktop application that builds a ready-to-use, deduplicated library of 3D ligand structures for a given plant. It queries multiple natural-product and chemical databases (COCONUT, LOTUS, Wikidata, PubChem, PlantaeDB, USDA Dr. Duke's, KNApSAcK and IMPPAT) in parallel, resolves each compound against PubChem for a canonical structure, and falls back to direct source retrieval when needed. Retrieved structures are then converted to 3D using RDKit, with configurable conformer generation and physicochemical filters (molecular weight, rotatable bonds, ring size, etc.). It can also process a manually supplied compound list, or convert an existing folder of 2D structures to 3D.
The following tool computes background parenchyma enhancement quantification on DCE-MRI at baseline and first time point with enhancement.