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

Advances in technologies related to biomedicine lead to the tremendous data generation through respective researches and discoveries. A wealth of knowledge obtained through these researches is available in the form of published texts. But it is a challenging task to extract relevant information like genes, proteins, diseases, drugs, biological mechanisms etc. from the available unstructured data [1]. Integration and analysis of the information obtained from the literature can lead to the new discoveries in medical health, medicine, therapeutic and diagnostic processes [2]. Therefore, this information could be easily extracted from the publications by using text mining approaches [3] and could be visualized using network biology approaches. Text mining is the branch of data mining which is used to find hidden relevant information from the available published corpora [4]. The purpose of text mining here is to process the unstructured textual information related to biofilms in order to extract knowledge like set of biofilm genes, their interactions with biofilm biological processes and associated drug relationship [5]. The application of text mining has earlier been seen on the literature of biomedical sciences like respiratory disease, breast cancer, heart diseases etc [6].