Candidate Gene Prioritization Using Unique Pattern Indexing and Mapping Techniques

Abstract

“Prioritizing the candidate gene is amongst the notable work in bioinformatics. Techniques have been applied to reduce the number of promising genes for a certain disease. Previous works were done by using PageRank and HITS algorithm on graph based network. However using frequent pattern mining this prioritizing can be made more efficient. In this paper, we propose four algorithms. The first one indexes the unique sequences of length four using an integer value. The second algorithm finds the frequency of the frequent patterns of various lengths by searching through the integer values instead of the patterns themselves. Third one weights the candidate gene in compare with the genes of database. Fourth algorithm creates the graph network and ranks the candidate gene. All this is done highly efficiently by the use of mapping techniques e.g. HashMap. Due to its highly frugal nature, the proposed algorithm can reduce typical memory usage by 37.5% at the very minimum.”