Data mining database searches

A variety of methods have been developed by mathematicians and computer scientists to address this task, which has become known as data mining (see Chapter 9, Section 9.8). Fayyad defined and described the term data mining as the nontrivial extraction of impHcit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases [16]. In order to extract information from huge quantities of data and to gain knowledge from this information, the analysis and exploration have to be performed by automatic or semi-automatic methods. Methods applicable for data analysis are presented in Chapter 9. 

Prakash O, Ghosh I (2006) Developing an antituberculosis compounds database and data mining in the search of a motif responsible for the activity of a diverse class of antituberculosis agents. J Chem Inf Model 46 17-23... 

The power of the currently available methods for proteome mining can be illustrated with data reported on the identification of 800 proteins from 50 milhon E. coli K12 cells using multiple fractionation, RPLC-MS-MS, and various ways of database searching [37]. With MASCOT searching, 754 proteins were identified from 1326 peptides and 2167 MS-MS spectra, which means on average 1.75 peptides and 2.87 spectra per protein. Abont 17% of the E. coli K12 proteome was covered in this way. 

Data mining has been defined as The nontrivial extraction of implicit, previously unknown and potentially useful information from data (Frawley et al., 1992). However, an easier way to grasp the concept of data mining is to think of it as a process that uses automated, analytic tools to search large databases, in order to discern useful information. 

Bioinformatics—the collection, organization, visualization, and analysis of large amounts of biological data, using computers and databases. Bioinformatics also includes the integration and mining (detailed searching) of databases. 

The task of data mining in a chemical context is to evaluate chemical data sets in search of patterns and common features to find information that is somehow inherent to the data set but not obvious. One of the differences between data mining and conventional database queries is that the characterization of chemicals is performed with the help of secondary data that are able to categorize data in a more general way and helps in finding patterns and relationships. It would be an unsuccessful approach to try to keep all potentially useful information about a chemical substance in a structure database. Thus, the extraction of relevant information from multiple data sources and the production of reliable secondary information are important for data mining. 

Data mining can be defined as a process of exploration of large amounts of data in search of consistent patterns, correlations, and other systematic relationships between queries and database objects. The tasks of a data mining engine can be divided into the following classes. 

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