Information retrieval in life sciences

Information extraction from clinical and biological data requires complex algorithms for data handling that are generally based on machine-learning and data-mining techniques. In the last years, the interest for the extraction and analysis of knowledge in life sciences is in growing; the biological resources are available in several formats characterized by heterogeneous models, thus bioinformatics algorithms are necessary to handle and to integrate the data, as well as to analyze the results in order to make these context-specific. Novel data structures are needed for storage and retrieval, while efficient algorithms are necessary for managing and analysis. An overview of solutions used in life science for knowledge retrieving, as well as methods for data integration and analysis based on machine-learning and data-mining techniques are presented.