Text Mining for Translational Bioinformatics
Abstract
Translational bioinformatics is an emerging field with a fascinating aim to develop novel computational techniques to facilitate traditional translational research through the convergence of molecular bioinformatics, biostatistics, statistical genetics, and clinical informatics. Translational bioinformatics is now more powerful than ever and has become a clinch between biological findings and clinical informatics. The computational techniques contribute by integrating multidimensional data consisting of medications, diseases, and genomes with clinical and pathological features. They are applied in various aspects with the hope of uncovering therapeutic targets and biomarkers of patient response. However, the accumulation of rich data from past studies, advancement of new experimental techniques, and ease of access to publications nowadays result in enormous repositories of scientific literatures and biomedical data, hindering the translation of molecular understandings into technologies that could impact patients. Text mining is an established field, but its application for translational bioinformatics is still a novel research direction with enormous research potential. The present issue emphasizes the application of text mining on biomedical/clinical publications and knowledge bases to facilitate the discovery and management of translational medical research knowledge.
Rapid growth of disease related biomedical literature makes the traditional information retrieval…
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