Prediction of dominant genes responsible for Lung Adenocarcinoma using Rough Set Theory
Abhinandan Khan, Goutam Kumar Saha, Srirupa Dasgupta, Soumya Kanti Datta · Graduate School and Research Center in Digital Science (EURECOM) · 2011
This paper presents an efficient approach of predicting the dominant genes responsible for Lung Adenocarcinoma using Rough Set Theory. The work takes a microarray dataset containing data of diseased, suspected and healthy patients and characterizes them in terms of objects and attributes. Using rough set theory, redundant attributes are then determined and eliminated. The core attributes are worked out by analyzing the relationship among the remaining attributes. Then Johnson's reduction algorithm has been used to extract underlying important rules from the remaining dataset. The paper reports three sets of rules, one each for diseased, suspected and healthy persons. The dominant genes can be accurately predicted by investigating the genes appearing in the generated Rule Sets. Microarray data obtained from a patient is analyzed in accordance with the Rule Sets generated. If any match is found with any one of the mentioned three cases, the patient will be diagnosed accordingly.