ISSN: 2041-6792

Clinical Investigation

Journals List Of Datamining In Proteomics

Data mining plays a role in the middle of this process. Overall, the focus is on identifying opportunities and developing computational solutions (including algorithms, models, tools, and databases) that can be used for experimental design, data analysis and interpretation, and hypothesis generation. There is no doubt that both computational biology and bioinformatics, and the interface of computer science and biology in general, are central to the future of biological research. The disciplines span a process that begins with data collection, analysis, classification, and integration, and ends with interpretation, modeling, visualization, and prediction. Hand and Heard present in their review article various tools for finding relevant subgroups in gene expression data. Alkharouf et al conduct an OLAP cube (online analytical processing) to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, which is a devastating pest of soybean. Brylinski et al created a sequence-to-structure library based on the complete PDB database. Then an early-stage folding conformation and information entropy were used for structure analysis and classification. Whilst postgenomic science is producing vast data torrents, it is well known that data do not equal knowledge and so the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. More sophisticated data mining strategies are needed for mining such high-dimensional data to generate useful relationships, rules, and predictions.    

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