Frequently Asked Questions

GeDiPNet (Gene Disease Pathway Network) is an open-access database that integrates gene-disease associations from various sources, offering curated information on human genes, diseases, SNPs, and pathways. It supports efficient analysis of gene-disease relationships and is designed for high-throughput analysis to identify insights into genetic disease associations, polypharmacological targets, and gene-disease pathway networks. GeDiPNet is a comprehensive resource for exploring gene-disease interactions, helping researchers uncover novel insights in genetic disease research and precision medicine.

Yes. GeDiPNet is a fully open-access resource: searching, browsing genes/diseases/pathways, and running all analysis tools is free for everyone, with no subscription or paywall.

No account is needed to search, browse, or run analyses. You only need to register (free and instant) if you want to submit a new gene-disease association or generate an API key for programmatic access.

Studying gene-disease associations helps understand disease mechanisms, identify diagnostic biomarkers, inform personalized medicine, and enable risk assessment and predictive medicine.

Use the search bar in the navigation bar on any page and start typing a gene symbol or disease name, and GeDiPNet will suggest matches as you type. You can also browse the complete gene and disease lists from the Browse menu.

GeDiPNet provides comprehensive information on human genes, including gene-disease associations, protein domain/family data, pathway analysis, functional annotations, gene expression, regulation, genetic variations, and protein-protein interactions.

GeDiPNet provides six analysis tools: enrichment analysis (pathway, disease-genes, disease-GO, tissue-specific, and domain) for identifying statistically significant gene-disease associations; comorbidity analysis for scoring how related two diseases (or genes) are, across shared genes, shared phenotypes, and tissue expression overlap; Venn analysis for examining overlaps in genes, pathways, gene ontologies, or Pfam domains (or your own custom lists) across up to 6 sets at once; polypharmacological target prediction (including a Drug Deep-Dive into clinical trial status, tissue expression, and side effects for a specific candidate drug) for multi-target therapeutics; and two data-driven discovery tools, Disease Clusters and Shared-Gene Disease Pairs, for finding disease relationships you didn't already have in mind.

Both are computed from the same underlying shared-gene similarity data, but answer different questions. Shared-Gene Disease Pairs ranks every pair of diseases by how much curated gene overlap they share, as a one-to-one comparison. Disease Clusters groups many diseases together via label propagation over that same similarity graph, so membership can come from transitive overlap (A links to B, B links to C) rather than just a direct pairwise match, which makes it useful for spotting multi-disease groups, not just pairs.

Since 8 October 2026, the Gene Network Analysis and Polypharmacological Target network analyses report harmonic closeness centrality: the average of 1/distance from a gene to every other gene in the network, with genes it cannot reach counting as 0, giving a value between 0 and 1. Earlier results used standard closeness centrality, which only counts the genes a node can reach, so a gene in a small separate cluster (for example, an isolated pair of genes) could score 1 and look more central than genes in the main network. Closeness values (and the hub labels that depend on them) from before this date are therefore not directly comparable with current results. Degree and betweenness centrality are calculated the same way as before.

Yes. The chat icon in the bottom-right corner of every page opens GeDiPNet's built-in assistant. Ask it plain-language questions like "What genes are linked to Diabetes?" or "How do I interpret this page?" and it answers using GeDiPNet's own curated data, grounded in whatever page and results you're currently looking at.

Yes, if you're logged in. Every analysis results page has a Save This Analysis button that stores your exact inputs so you can reopen the results later without re-entering everything. If you reopen a saved analysis and rerun it, the same button switches to Update Saved Analysis to overwrite it in place instead of creating a duplicate. Individual gene, disease, pathway, protein, and SNP detail pages also have their own Bookmark button. Everything you save is listed under My Saved Analyses on your profile page.

GeDiPNet curates and cross-references data from established resources, including NCBI Gene, HGNC, and Ensembl for gene information; DisGeNET, OMIM, ClinVar, and Orphanet for disease associations; KEGG and Reactome for pathways; UniProt and STRING for proteins; and PubMed for supporting literature. See the full list on the Resources page.

Sub-types or similar disease names are merged into unique terms for efficient analysis.

Log in (or create a free account) and open your profile page. Submitting needs submitter access: apply for it from your profile, and once it's approved, choose Submit Gene-Disease Data under Quick Actions and fill out the form with the gene, disease, and supporting evidence details. Fields marked with an asterisk (*) are required.

You'll receive a confirmation with a submission ID right away. Each submission is then reviewed by our curation team before it's added to the database. If you'd like an update on a specific submission, reach out via the Contact Us page and reference your submission ID.

Yes. Registered users can generate a free API key from their profile page and use it to query gene-disease data programmatically. Each key is limited to 100 requests per hour. See the API documentation for endpoints, request/response formats, and code examples.

GeDiPNet is regularly updated to ensure the latest research and data are included.

Kundu I, Sharma M, Barai RS, Pokar K, Idicula-Thomas S (2023). GeDiPNet: Online resource of curated gene-disease associations for polypharmacological targets discovery. Genes & Diseases, https://doi.org/10.1016/j.gendis.2022.05.034

Contact our support team at biomedinfo@nirrch.res.in