MSLP: mRNA subcellular localization predictor based on machine learning techniques.

ABSTRACT:

Background

Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.

Methods

In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algor

SUBMITTER: Musleh S 

PROVIDER: S-EPMC10035125 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature