All of our software, tools, datasets, etc. are 100% open-source, and free for anyone around the world to use and modify. We strive to make resources that are high quality in every aspect: cleanly written, robustly constructed and tested, well-documented, easy-to-use, accessible, customizable, and as effective as possible in real-world use.
Adage helps you explore gene expression data and discover new insights from machine learning models. See how genes relate to each other, other genes, and how they are represented in machine learning signatures. View the activities of samples as a colored heatmap, and cluster them on the fly. Explore the genes that contribute to signatures, the experiments in which they vary the most, and the pathways they significantly overlap with.
Preprint Similarity Search uses a machine learning model trained on 1.7 million PubMed Central open access documents to find similar papers and journals based on the textual content of your bioRxiv or medRxiv preprint. These results can be used as a starting point when searching for a place to publish your paper.
Connectivity search allows you to find the most important paths connecting any two nodes in Hetionet, even when they are different types or not known to be related. If you want to investigate how the eye and breast cancer may be related, you can. Filter nodes by type, see computed significance data about paths, create graph visualizations with highlighted paths, and more.
Manubot is a workflow and set of tools for the next generation of scholarly publishing. Write your manuscript in markdown, track it with git, automatically convert it to .html, .pdf, or .docx, and deploy it to your destination of choice.
Seamlessly mine data across bioinformatics systems. Tribe stores the findings of your research as collections of genes.
IMP helps researchers identify new gene-gene and gene-process associations that are supported by existing data but currently unannotated.
GIANT helps researchers identify which genes are working together in specific tissues. This tissue-specific lens into human biology helps researchers study complex human diseases.
Nano-dissection allows researchers to identify genes expressed in specific tissues and cell lineages from microarray data that are not resolved to the level of the cell lineage of interest.