Managing Biological Networks by Using Text Mining and Computer-aided Curation

Abstract

In order to understand a biological mechanism in a cell, a researcher should collect a huge numberof protein interactions with experimental data from experiments and the literature. Text miningsystems that extract biological interactions from papers have been used to construct biologicalnetworks for a few decades. Even though the text mining of literature is necessary to constructa biological network, few systems with a text mining tool are available for biologists who wantto construct their own biological networks. We have developed a biological network constructionsystem called BioKnowledge Viewer that can generate a biological interaction network by using atext mining tool and biological taggers. It also Boolean simulation software to provide a biologicalmodeling system to simulate the model that is made with the text mining tool. A user can downloadPubMed articles and construct a biological network by using the Multi-level Knowledge EmergenceModel (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text miningtool. To evaluate the system, we constructed an aging-related biological network that consist 9,415nodes (genes) by using manual curation. With network analysis, we found that several genes,including JNK, AP-1, and BCL-2, were highly related in aging biological network. We providea semi-automatic curation environment so that users can obtain a graph database for managingtext mining results that are generated in the server system and can navigate the network withBioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr