Text Mining and Network Analysis for Discovery of Novel Genes Associated with Staphylococcus Biofilms

Abstract

Text mining is the process of extracting relevant information from the unstructured data obtained from the published corpora in biomedical literature. A vast amount of literature is available in context of biofilms in PubMed database. It is a very daunting task to extract useful information in the form of genes and proteins from text manually; therefore, text mining approaches are used for this purpose. The extracted relevant information can be appropriately visualized using network biology approaches. Therefore, here we present a framework that would help in discovering novel biomarkers (genes) and gene-drug associations, involved in Staphylococcus biofilm using the available published corpora. We initially utilized COREMINE tool to select biological processes (i.e. biofilm formation, growth, quorum sensing and pathogenesis) and extract genes from PubMed literature database for biofilms. We selected the co-occurring genes for four biofilm processes through network biology approach and validated them by using GO enrichment analysis. AgrB gene was validated as the gene involved in two biological processes i.e. biofilm quorum sensing and pathogenesis. Therefore, structure of AgrB encoded protein (AgrB) was predicted using bioinformatics tools and analysis of ligand binding was done using molecular docking which was further used in blocking quorum sensing and pathogenesis processes. The validation of results obtained from text mining suggests that this approach can be extended to reveal interesting trends and associations among different biological entities related to biofilms and other diseases.

Integrating Text Mining and Network Analysis for Topic Detection from Published Articles on Banana Sensory Characteristics

“…Text mining uses the Natural Language Processing (NLP) tools for the automatic discovery of previously unknown information from unstructured data [7,6] typically consisting of four stages (a) information retrieval by gathering a set of textual materials for a given topic; (b) entity recognition characterised by identifying textual features from gathered texts; (c) information extraction which aims to extract relationships among the recognised textural features such as occurrence and co-occurrence of specific terms (indexing) and (d) knowledge discovery, the extracted relationships are used to identify useful patterns from the data set [8]. Network analysis is a sociology techniques used to study the relationships and community structures in social data and has since been applied in other fields such as bioinformatics in order to find key molecular markers and communities within an interaction network [8].…”