Translational Bioinformatics aims to develop novel computational techniques to facilitate traditional translational research through the convergence of molecular bioinformatics, biostatistics, statistical genetics, and clinical informatics. The computational techniques work 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. 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 literature and biomedical data. In light of the emerging big data, this Text Mining for Translational Bioinformatics special issue will emphasize the application of text mining on biomedical/clinical publications and knowledge bases to facilitate the discovery and management of knowledge. For example, text mining techniques can be employed to explore knowledge about genotypic associations and provide genomic literature evidences to support the phenotype associations. Through this special issue, we hope to propel translational research in a less time- and effort- consuming manner by reducing the cost of manpower and experimental materials, thus benefiting translation-oriented studies and improving the life quality of mankind.

Potential topics include, but are not limited to:

• Ontologies for translational bioinformatics
• Natural language processing approaches on medical/healthcare text
• Named-entity recognition, normalization, and coreference resolution for biomedical concepts
• Text-based approach to automated protein function prediction and relation extraction
• Text mining approaches that use translational genomics, epigenomics, and transcriptomics data
• Novel visualization of biocuration assistance in biomedical literature
• Reviews of existing text mining techniques/applications in translational bioinformatics
• Applications of high performance computing or MapReduce-based natural language processing for translational bioinformatics
• Big data analytics on biomedical text for translational bioinformatics
• Text mining and understanding of cancer signaling pathways