An Editorial Note on Data Mining in Genomics

Chinthala Mounica^*
*Correspondence: Chinthala Mounica, Department of Computer Science, Chaitanya University, Warangal, Telangana, India, Email:

Editorial

Data mining is a process which finds useful patterns from large amount of data. few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results.

Data mining for genomics belongs to an interdisciplinary and relatively new field of bioinformatics, which evolves so rapidly that it is difficult to predict the extent and pace of the changes. Biology, or more generally life sciences, can now be considered information sciences. They are changing from disciplines that deal with relatively small data sets to research fields overwhelmed by a large number of huge data sets. Two main triggers are the source of these changes. The first was the Human Genome Project. As the result of research sparked by this project, we now have a large and growing library of organisms with already sequenced genomes. The second was a new technology— genomic microarrays—that allows for the quick and inexpensive measurement of gene expression level for thousands of genes simultaneously

Data mining is a logical process that is used to search through large amount of data in order to find useful data. The goal of this technique is to find patterns that were previously unknown. Once these patterns are found they can further be used to make certain decisions for development of their businesses.

Three steps are involved:

• Exploration

• Pattern identification

• deployment

Data mining applications

      Data mining is a relatively new technology that has not fully matured. Despite this, there are a number of industries that are already using it on a regular basis. Some of these organizations include retail stores, hospitals, banks, and insurance companies. Many of these organizations are combining data mining with such things as statistics, pattern recognition, and other important tools. Data mining can be used to find patterns and connections that would otherwise be difficult to find. This technology is popular with many businesses because it allows them to learn more about their customers and make smart marketing decisions.

Here is overview of business problems and solutions found using data mining

Challenges:

• To reduce direct mail costs.

• Increase efficiency of marketing campaigns.

• Increase cross-selling to existing customers, using inbound channels such as the company’s sell center and the internet a one year test of the solution’s effectiveness.