Journal Article

Random forest-integrated analysis in AD and LATE brain transcriptome-wide data to identify disease-specific gene expression

Cheng, Qiang, Peng, Chong, Wu, Xinxing, Nelson, Peter T.

2021

Overview

Alzheimer’s disease (AD) is a complex neurodegenerative disorder that affects thinking, memory, and behavior. Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently identified common neurodegenerative disease that mimics the clinical symptoms of AD. The development of drugs to prevent or treat these neurodegenerative diseases has been slow, partly because the genes associated with these diseases are incompletely understood. A notable hindrance from data analysis perspective is that, usually, the clinical samples for patients and controls are highly imbalanced, thus rendering it challenging to apply most existing machine learning algorithms to directly analyze such datasets. Meeting this data analysis challenge is critical, as more specific disease-associated gene identification may enable new insights into underlying disease-driving mechanisms and help find biomarkers and, in turn, improve prospects for effective treatment strategies. In order to detect disease-associated genes based on imbalanced transcriptome-wide data, we proposed an integrated multiple random forests (IMRF) algorithm. IMRF is effective in differentiating putative genes associated with subjects having LATE and/or AD from controls based on transcriptome-wide data, thereby enabling effective discrimination between these samples. Various forms of validations, such as cross-domain verification of our method over other datasets, improved and competitive classification performance by using identified genes, effectiveness of testing data with a classifier that is completely independent from decision trees and random forests, and relationships with prior AD and LATE studies on the genes linked to neurodegeneration, all testify to the effectiveness of IMRF in identifying genes with altered expression in LATE and/or AD. We conclude that IMRF, as an effective feature selection algorithm for imbalanced data, is promising to facilitate the development of new gene biomarkers as well as targets for effective strategies of disease prevention and treatment.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Age / Aged / Aged, 80 and over / Algorithms / Alzheimer Disease - diagnosis / Alzheimer Disease - diagnostic imaging / Alzheimer Disease - genetics / Alzheimer Disease - pathology / Alzheimer's disease / Analysis / Biology and Life Sciences / Biomarkers / Biomarkers - metabolism / Brain - metabolism / Brain - pathology / Brain Diseases - diagnosis / Brain Diseases - diagnostic imaging / Brain Diseases - genetics / Brain Diseases - pathology / Cognitive Dysfunction - diagnosis / Cognitive Dysfunction - diagnostic imaging / Cognitive Dysfunction - genetics / Cognitive Dysfunction - pathology / Comorbidity / Computer and Information Sciences / Data analysis / Datasets / Decision trees / Dementia / Diagnosis, Differential / DNA-Binding Proteins - genetics / Drug development / Encephalopathy / Feature selection / Female / Gene expression / Gene Expression Regulation - genetics / Genes / Genetic aspects / Health services / Humans / Image Interpretation, Computer-Assisted / Learning algorithms / Machine Learning / Magnetic Resonance Imaging / Male / Medicine and Health Sciences / Neurodegenerative diseases / Neurodegenerative Diseases - diagnosis / Neurodegenerative Diseases - diagnostic imaging / Neurodegenerative Diseases - genetics / Neurodegenerative Diseases - pathology / Physical Sciences / Research and Analysis Methods / Signs and symptoms / Transcriptome - genetics / Transcriptomes