Logistic regression paradigm for training a single-hidden layer feedforward neural network. Application to gene expression datasets for cancer research.
Journal:
Journal of biomedical informatics
Published Date:
Dec 31, 2019
Abstract
OBJECTIVE: The speed of the diagnosis process is vital in pursuing the trial of curing cancer. During the last decade, precision medicine evolved by detecting different types of cancer through microarrays (MA) of deoxyribonucleic acid (DNA) processed by machine learning (ML) algorithms. Personalized diagnosis, followed by personalized treatment, should imply personalized hyperparameters of the ML. The goal of this paper is to propose a novel adaptive ML method that embeds knowledge into the architecture of the algorithm and also filters the features in order to reduce their number, increase computational speed, and decrease computational cost and time.