A deep belief network-based method to identify proteomic risk markers for Alzheimer disease

TL;DR

This paper introduces a deep belief network-based feature selection method that effectively identifies proteomic risk markers for Alzheimer disease, achieving high diagnostic accuracy and revealing potential therapeutic targets.

Contribution

It presents a novel feature selection approach using deep belief networks to identify proteomic risk markers for Alzheimer disease, outperforming traditional methods.

Findings

Achieved over 90% diagnostic accuracy.

Identified new proteomic risk markers linked to Alzheimer.

Suggested apidonectin-linked pathways as therapeutic targets.

Abstract

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target.