DATA MINING IN BIOINFORMATICS |CREATIVE IDEOLOGY ON ENGINEERING| BIOINFORMATICS

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Bioinformatics deals with the storage, gathering, simulation and analysis of biological data for the use of informatic tools such as data mining.
Data mining, or knowledge discovery from data (KDD), is the process of uncovering trends, common themes or patterns in “big data”. … For example, an early form of data mining was used by companies to analyze huge amounts of scanner data from supermarkets “Mining biological data helps to extract useful knowledge from massive datasets gathered in biology, and in other related life sciences areas such as medicine and neuroscience.

Applications of data mining to bioinformatics include gene finding, protein function domain detection, function motif detection, protein function inference, disease diagnosis, disease prognosis, disease treatment optimization, protein and gene interaction network reconstruction, data cleansing, and protein subcellular Data Mining Specialists are responsible for designing various data analysis services to mine for business process information. … This individual is also responsible for building, deploying and maintaining data support tools, metadata inventories and definitions for database file/table creation The two “high-level” primary goals of data mining, in practice, are prediction and description. The main tasks wellsuited for data mining, all of which involves mining meaningful new patterns from the data, are: Classification: Classification is learning a function that maps (classifies) a data item into one of several predefined
classes.
Estimation: Given some input data, coming up with a value for some unknown continuous variable.
Prediction: Same as classification & estimation except that the records are classified according to some future behavior or estimated future value).
Association rules: Determining which things go together, also called dependency modeling.
Clustering: Segmenting a population into a number of subgroups or clusters.
Description & visualization: Representing the data using visualization techniques.
Learning from data falls into two categories: directed (“supervised”) and undirected (“unsupervised”) learning. The first three tasks – classification, estimation and prediction – are examples of supervised learning. The next three tasks – association rules, clustering and description & visualization – are examples of unsupervised learning. In
unsupervised learning, no variable is singled out as the target; the goal is to establish some relationship among all
the variables. Unsupervised learning attempts to find patterns without the use of a particular target field.
The development of new data mining and knowledge discovery tools is a subject of active research. One
motivation behind the development of these tools is their potential application in modern biology. Applications of data mining to bioinformatics include gene finding, protein function domain detection, function
motif detection, protein function inference, disease diagnosis, disease prognosis, disease treatment optimization,
protein and gene interaction network reconstruction, data cleansing, and protein sub-cellular location prediction.
For example, microarray technologies are used to predict a patient’s outcome. On the basis of patients’ genotypic
microarray data, their survival time and risk of tumor metastasis or recurrence can be estimated. Machine learning
can be used for peptide identification through mass spectroscopy. Correlation among fragment ions in a tandem
mass spectrum is crucial in reducing stochastic mismatches for peptide identification by database searching. An
efficient scoring algorithm that considers the correlative information in a tunable and comprehensive manner is
highly desirable Bioinformatics and data mining are developing as interdisciplinary science. Data mining approaches seem ideally
suited for bioinformatics, since bioinformatics is data-rich but lacks a comprehensive theory of life’s organization at
the molecular level.
However, data mining in bioinformatics is hampered by many facets of biological databases, including their size,
number, diversity and the lack of a standard ontology to aid the querying of them as well as the heterogeneous data
of the quality and provenance information they contain. Another problem is the range of levels the domains of
expertise present amongst potential users, so it can be difficult for the database curators to provide access
mechanism appropriate to all.
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