MTBGD: Mutli Type Biclustering for Genomic Data Biclustering of Genomic Data

摘要

Functional genomics has great impact in finding solutions for different diseases via studying the expression level of different genes, genome annotations, aberrant transcription detection, histone modification profiling and many more. Various methods are used these days to analyze the data generated by next generation sequencing and microarrays which are extensively used for translational bioinformatics research. Biclustering is a way to find similarities between two or more genes/loci as it consider local patterns which are missed by clustering methods. Researchers are developing different algorithms for biclustering among them BiSim is the one with time complexity of O(n*m). BiSim uses iterative approach to handle two dimensional data matrix by avoiding extra computations. We here by introduce MTBGD (Multi Type Biclustering for Genomic Data) and are putting one step further for enhancing the research related to biclustering by taking inspiration from BiSim. MTBGD also follows the iterative approach with time complexity O(m*n) with an additional advantage of identifying multiple types of biclusters by using a smart approach.