SUBGROUP DISCOVERY OF THE MODY GENES;

FROM TEXT DOCUMENTS

Abstract

Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to health
and economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is Maturity
Onset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date
11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documents
related to these genes in public domain database. Data Source: The data set was obtained from PubMed. Period: September-December,
2011. Materials and Methodology: APRIORI-SD subgroup discovery algorithm is used for the task of discovering subgroups. A well
known association rule learning algorithm APRIORI is first modified into classification rule learning algorithm APRIORI-C. APRIORI-C
algorithm generates the rule from the discretized dataset with the minimum support set to 0.42% with no confidence threshold. Total 580
rules are generated at the given support. APRIOIR-C is further modified by making adaptation into APRIORI-SD. Results: Experimental
results demonstrate that APRIORI discovers the substantially smaller rule sets; each rule has higher support and significance. The rules
that are obtained by APRIORI-C are ordered by weighted relative accuracy. Conclusion: Only first 66 rules are ordered as they cover the
relation between all the 11 MODY genes with each other. These 66 rules are further organized into 11 different subgroups. The evaluation
of obtained results from literature shows that APRIORI-SD is a competitive subgroup discovery algorithm. All the association among
genes proved to be true.