Find open-source science resources

DOAP is a project to create an XML/RDF vocabulary to describe software projects, and in particular open source projects.

Automated data visualization with minimal code

Parse scientific papers to structured fields (title/author/sections/references)

A collection of research papers for AI-based protein design.

Neural differential equations in PyTorch

Scalable gVCF merging and joint variant calling for population sequencing projects

Generative model for programmable protein design using diffusion modeling, equivariant graph neural networks, and conditional random fields to efficiently sample diverse all-atom structures; supports conditional generation via composable conditioners for substructure, symmetry, shape, and neural-network predictions; validated crystallographically (Generate Biomedicines, Nature 2023)

Large-scale PDF/LaTeX/JATS parsing to standardized JSON for millions of papers

Extract figures, tables, captions, and section titles from scholarly PDFs

First system to make novel, verifiable scientific discoveries by pairing LLMs with evolutionary search, solving open problems in combinatorics (cap set problem) and discovering faster matrix multiplication algorithms

DGL-LifeSci is a [DGL](https://www.dgl.ai/)-based package for various applications in life science with graph neural network.

Large language model for science

PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics.

Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached.

R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms.

JavaScript library that can be used to generate interactive and highly customizable web-based genome browsers.

BioJS is a library of over hundred JavaScript components enabling you to visualize and process data using current web technologies.

An ontology encoding the Common Information Model (CIM) schema

SWAN ontology consists in a collection of ontologies used to create, manage and share scientific knowledge bases.

Virtual machine with all software and sample data to run 3D-e-Chem Knime workflows

An ontology of histopathological morphologies used by pathologists to classify/categorise animal lesions observed histologically during regulatory toxicology studies. The ontology was developed using real data from over 6000 regulatory toxicology studies donated by 13 companies spanning nine species. The original structure of the histopathology ontology was designed ab initio when the [INHAND](http://www.goreni.org/) manuscripts were not available. However, the ontology has been repetitively reviewed and updated to align with the subsequently published INHAND manuscripts. During this process cross references to INHAND lesion identifiers were added to the ontology. [from GitHub]

This module provides a command line tool to validate DICOM SEG files against predefined requirements specified in an Excel file. It contains components for finding relevant DICOM files, loading and parsing validation requests and applying validation rules. The main validation process checks each DICOM file for compliance with the Type 1, 1C, 2, 2C and 3 attributes specified in the requirements file. A detailed report is generated highlighting issues such as missing, invalid or conditionally required attributes, including file paths and affected DICOM tags. The tool is designed to ensure data integrity and compliance with DICOM standards.

Automatically detects duplicate and near-duplicate DICOM image series in large medical imaging datasets. Uses a tiered pipeline combining DICOM metadata analysis, SHA-based pixel hashing, and image similarity metrics (SSIM, cosine, MAD) to identify exact copies, re-exported series, and near-identical acquisitions. All findings are reported for human expert review — no files are modified or deleted automatically. For scenarios requiring strict, image-level deduplication based on pixel content, fully agnostic to metadata changes, consider using [https://bio.tools/image_duplicate_check_tool]

Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.

A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools.

Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.

PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them.

Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage.

FlavoTyper is a bioinformatics tool that performs in silico serotyping of Flavobacterium psychrophilum genome assemblies.

MONAI Label is an intelligent open source image labeling and learning tool that enables users to create annotated datasets and build AI annotation models for clinical evaluation. MONAI Label enables application developers to build labeling apps in a serverless way, where custom labeling apps are exposed as a service through the MONAI Label Server.

xnattools is a python package with a set of modules for performing various operations on data stored in XNAT servers. The main purpose is to provide one standardized platform for running operations on XNAT servers. The package currently contains four tools that use this platform: dicom to nifty conversion, thumbnail generation from dicom, DICOM header data collection, in bulk downloading of a project.