Multi-view Text Mining for Disease Gene Prioritization and Clustering

Abstract

AbstractText mining helps biologists to collect disease-gene associations automatically from large volumes of biological literature. During the past ten years, there was a surge of interests in automatic exploration of the biomedical literature, ranging from the modest approach of annotating and extracting keywords from text to more ambitious attempts such as Natural Language Processing, text-mining based network construction and inference. In particular, these efforts effectively help biologists to identify the most likely disease candidates for further experimental validation. The most important resource for text mining applications now is the MEDLINE database developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM). MEDLINE covers all aspects of biology, chemistry, and medicine, there is almost no limit to the types of information that may be recovered through careful and exhaustive mining [45]. Therefore, a successful text mining approach relies much on an appropriate model. To create a text mining model, the selection of Controlled Vocabulary (CV) and the representation schemes of terms occupy a central role and the efficiency of biomedical knowledge discovery varies greatly between different text mining models. To address these challenges, we propose a multi-view text mining approach to retrieve information from different biomedical domain levels and combine them to identify the disease relevant genes through prioritization. The view represents a text mining result retrieved by a specific CV, so the concept of multi-view text mining is featured as applying multiple controlled vocabularies to retrieve the gene-centric perspectives from free text publications. Since all the information is retrieved from the same MEDLINE database but only varied by the CV, the term view also indicates that the data consists of multiple domain-based perspectives of the same corpus. We expect that the correlated and complementary information contained in the multi-view textual data can facilitate the understanding about the roles of genes in genetic diseases.KeywordsText MiningRand IndexLatent Semantic IndexingKEGG OrthologyGene PrioritizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.