PUB - Publikationen an der Universität Bielefeld

Discovery of novel genes that control important phenotypes and diseases is one of the key challenges in biological sciences. Now, in the post-genomics era, scientists have access to a vast range of genomes, genotypes, phenotypes and ‘omics data which - when used systematically - can help to gain new insights and make faster discoveries. However, the volume and diversity of such un-integrated data is often seen as a burden that only those with specialist bioinformatics skills, but often only minimal specialist biological knowledge, can penetrate. Therefore, new tools are required to allow researchers to connect, explore and compare large-scale datasets to identify the genes and pathways that control important phenotypes and diseases in plants, animals and humans.

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. The KnetMiner approach is based on 1) integration of heterogeneous, complex and interconnected biological information into a knowledge graph; 2) text-mining to enrich the knowledge graph with novel relations extracted from literature; 3) graph queries of varying depths to find paths between genes and evidence nodes; 4) evidence-based gene rank algorithm that combines graph and information theory; 5) fast search and interactive knowledge visualisation techniques. Overall, [KnetMiner](http://knetminer.rothamsted.ac.uk) is a publicly available resource that helps scientists trawl diverse biological databases for clues to design better crop varieties and understand diseases. The key strength of KnetMiner is to include the end user into the “interactive” knowledge discovery process with the goal of supporting human intelligence with machine intelligence.