Available KnetMiners and knowledge networks

About KnetMiner

KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools for integrating and visualising large biological datasets. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes and publications with the aim to provide leads for scientists who are investigating the molecular basis for a particular trait.

Building knowledge networks

Knowledge networks or graphs provide a perfect data structure for heterogeneous, complex and interconnected biological information and are built using the open-source Ondex data integration platform. A knowledge network consists of labelled nodes, such as a gene, pathway, trait, publication, that are connected through labelled edges, such as encodes, interacts, published-in. Visit our wiki and read  Hassani-Pak et al. (2016) to learn how we build knowledge networks.

Searching knowledge networks

KnetMiner performs over 70 graph queries of varying depths to find direct or indirect links between genes and user provided search terms. It is very fast through an in-house developed graph database and graph-indexing techniques. This enables KnetMiner to have unique features like real-time user feedback while typing search terms and advanced query term suggestions.

Deploy your own KnetMiner in the cloud

KnetMiner is available as an App in the commercial, cloud-based Genestack bioinformatics platform. The KnetMiner-Genestack App allows users to integrate their own private data into the knowledge graph and run their own KnetMiner server in the cloud. For more information, read our press release and request the KnetMiner-Genestack eBook.

Candidate gene prioritisation

Our evidence-based gene rank algorithm combines key properties of the knowledge graph with well established concepts from information theory. It takes into account the specificity of each evidence and the frequency of all gene-evidence paths. KnetMiner is unique as it allows users to see how and why the prediction was made by visualising the provenance.

Working with us

Get in touch with Keywan Hassani-Pak if you like to collaborate or if you are looking for jobs, internships, MSc or PhD projects. We have close links with academic partners around the world such as UWA (Australia), IPK (Germany), Cornell (USA), INTA (Argentina) and IRRI (Philippines). If you are non-academic, we can provide evaluation licenses and consultancy to help you get started.