The Impact of Computer Science in Molecular Medicine: Enabling High- Throughput Research

Diana      de la Iglesia; Miguel      Garcia-Remesal; Guillermo      de la Calle; Casimir      Kulikowski; Ferran      Sanz; Victor      Maojo

doi:10.2174/1568026611313050002

Abstract

The Human Genome Project and the explosion of high-throughput data have transformed the areas of molecular and personalized medicine, which are producing a wide range of studies and experimental results and providing new insights for developing medical applications. Research in many interdisciplinary fields is resulting in data repositories and computational tools that support a wide diversity of tasks: genome sequencing, genome-wide association studies, analysis of genotype-phenotype interactions, drug toxicity and side effects assessment, prediction of protein interactions and diseases, development of computational models, biomarker discovery, and many others. The authors of the present paper have developed several inventories covering tools, initiatives and studies in different computational fields related to molecular medicine: medical informatics, bioinformatics, clinical informatics and nanoinformatics. With these inventories, created by mining the scientific literature, we have carried out several reviews of these fields, providing researchers with a useful framework to locate, discover, search and integrate resources. In this paper we present an analysis of the state-ofthe- art as it relates to computational resources for molecular medicine, based on results compiled in our inventories, as well as results extracted from a systematic review of the literature and other scientific media. The present review is based on the impact of their related publications and the available data and software resources for molecular medicine. It aims to provide information that can be useful to support ongoing research and work to improve diagnostics and therapeutics based on molecular–level insights.

Keywords: Biomedical Informatics. Computational Resources. Data sources. Genotype-Phenotype. Molecular Medicine. Medicinal Chemistry. Personalized Medicine.

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