Issues in mining skills: Application to computational biology domain

The emergence of database management systems has provided essential requirements for analysis and data handling. Data Mining refers to the nontrivial extraction of implicit, but potentially useful information from data present in repositories. It is the process for the automatic extraction of patterns, associations, changes, anomalies, and significant structures from data. These methods of data mining are typically used in combination with each other, either in parallel or as part of a sequential operation. The increase in various life science repositories such as PIR, GenBank reflect the work of thousands of researchers in laboratories around the world who are engaged in mass producing biological data. There are more data to deal with today because modern researchers are using computer-enabled, data-centric, high-throughput processes, such as automated sequencing machines and microarray. Specifically, data mining is data driven, and is most typically used for statistical data analysis and knowledge discovery. In this book I have tried to cover some current trends and aspects of mining in computational biology domain.

Autorentext

Somnath Tagore is Assistant Professor at Department of Biotechnology and Bioinformatics, Dr. DY Patil University, Navi Mumbai, India. He has done M.Sc, M.Tech, M.Phil and currently pursuing Ph.D in areas of Systems Biology. His research area mainly includes data mining, in-silico metabolic engineering, algorithms and graph theory.