A Curvature Guided Composite Kernel Framework for Differential Gene Selection in Cancer Transcriptomics

Abstract

Identification of differentially expressed genes is a crucial step for downstream tasks on gene data such as biomarker discovery, drug target identification. Traditional Methods assume negative binomial distribution on RNA-sequence data and models the DEGs using either generalized linear models or by estimating dispersion and assumption of mean-variance rate. The proposed method uses axiomatic approach by using quantum mechanics principles to project transcript data onto a Hilbert space using a composite kernel. Using the curvature generated by the transcripts on the latent manifold within the Hilbert space, a gravitational search inspired mechanism is used to identify the optimal number of differentially expressed genes by minimizing a representational loss function, and a reduced gene feature space is constructed as the potential differentially expressed genes. The proposed method has been compared with existing empirical methods for validation using proper statistical and biological benchmark analysis.