Title: Functional module extraction by ensembling the ensembles of selective module detectors

Authors: Monica Jha; Pietro Hiram Guzzi; Pierangelo Veltri; Swarup Roy

Addresses: Department of Information Technology, North Eastern Hill University, Shillong, 793022, India ' Department Surgical Medical Sciences and Data Analytics Research Centre, University of Catanzaro Italy, 88100, Italy ' Department Surgical Medical Sciences and Data Analytics Research Centre, University of Catanzaro Italy, 88100, Italy ' Department of Computer Applications, Sikkim University, Gangtok, Sikkim, 737102, India

Abstract: A group of functionally related genes constitutes a functional module taking part in similar biological activities. Such modules can be employed for the interpretation of biological and cellular processes or their involvement in associated diseases. Detection of such modules from gene expression data is a difficult task, but important from system biology point of view. Different module detectors have been proposed for a few decades with their relative merits and demerits. They can be broadly classified as Clustering, Bi-Clustering and Network based. In this work, we try to combine the merits of some of the selective module detectors picked from three types of module detectors. We perform a two-level ensemble by unifying the goodness of different module detectors. For our experimentation, we use RNAseq read counts as a measure of gene expression. We compare ensemble outcomes with state-of-the-art module detectors and observe a superior performance in comparison to them.

Keywords: functional module; ensemble; clustering; bi-clustering; network module; RNA sequence.

DOI: 10.1504/IJCBDD.2019.103599

International Journal of Computational Biology and Drug Design, 2019 Vol.12 No.4, pp.345 - 361

Received: 04 Jul 2018
Accepted: 19 Nov 2018
Published online: 13 Nov 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article