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Techniques in DNA Data Mining

The main concern of data mining is analysis of data. Its main objective is to detect patterns automatically in any data set through minimum user input and efforts. There is a vast set of data mining tools and techniques which can be applied in varied fields or myriad forms. It can also be employed for making decision and for forecasting future trends of a market. Lately, several organizations have started using these tools for their data analysis. Applications of data mining are used in many industries including auditing, health industry, telecommunications, retail industry etc.

Recently, there is an increased use of data mining in the domain of medical science in branches like biomedical, DNA, genetics and medicine. In Genetics data mining helps in understanding the mapping relationship between variation in human DNA sequences and the susceptibility to diseases. Data mining tools assist in improving the varied medical stages like diagnosis of diseases, and their prevention and treatment.

An unprecedented spurt in biomedical research has demanded the study and analysis of enormous scale of gene patterns and their functions. There are countless ways in which data mining tools help the researches in biomedical domain and DNA data analysis.

List of Techniques in DNA Mining

There is a wide array of tools and techniques in Data Mining which are used in biomedical research, genetic study etc. These tools help in gaining more in-depth insights in data's knowledge exploration. Below listed are some famous and commonly used techniques:

• DNA data is generally unevenly distributed and disorganized in nature. For use in research, this heterogeneous data needs to be completely systematic and synchronized. Here, two tools of data mining are employed- Data Cleansing and Data Integration. These systematize the data and store it in proper data base for further reference.
• DNA data analysis has its most important application in comparison, in which varied data sequences are compared and similitude are looked for. The task involves one to note the difference in the gene sequence of healthy and diseased tissues. This is done by retrieving both the tissues' gene sequence, and then finding the patterns that are recurring in the entire data. This analysis tool helps in charting both similarities and dissimilarities in DNA genetic sequences.
• In the field of biomedical research, it is certified fact that commonly any disease occurs due to the combination of genes. For digging into this theory, Association Analysis Method, an important Data Mining tool is put to use. This technique helps in determining the co-occurrence of any particular group of genes. One can also find out the interaction and relationships between certain gene sequences.
• Regarding the same task, Path Analysis is also an important tool of Data Mining that is used in expansive biomedical researches and genetic study. Generally the differing combinations of genes that trigger any disease are activated at different stages of the ailment. Path Analysis links this odd combination of genes to the varied stages of development of the disease.
• Next comes- Visualization, a newly innovated technique of Data Mining, which plays a crucial role in biomedical Data Mining. As the name defines, this technique helps in presenting the various gene structures in the form of graphs, trees and chains. This visually attracting representation helps one to understand the complex gene structures quickly and in simplified manner. The technique adds to more discovery and exploration in the data.

Generally all the medical research centres and genetic study houses have their technical team which works through all these techniques for arranging and managing the research data efficiently. But nowadays out of the financial circumstances, most of these medical facilities are opting for DNA Sequencing Data Mining Outsourcing. The research centres carry out their researches while they dole out their accumulated data to the hired companies which with the help of Data Mining tools to manage their data.