Bioengineering
Nested Evaluation of Sparse Multi-Objective Evolutionary Gene-Panel Discovery for Diabetic Nephropathy
By Cong Y, Wu T, Yang X and 4 others.
Abstract
Background
Diabetic nephropathy gene panels come from small kidney transcriptome cohorts, where screening and adaptive search inflate performance. Evolutionary multi-objective search balances panel size, accuracy, and biological coherence, but dense initialization slows it, and its benefit over ranking or regularized regression is rarely tested under nested evaluation.
Methods
Six evolutionary algorithms optimizing leanness, inner balanced accuracy, and Gene Ontology (GO) coherence were evaluated with four repeats of nested five-fold cross-validation on GSE96804. Harmonic initialization was compared with Bernoulli, bounded uniform, and SparseEA initializers; GO was removed or randomized. Comparators were random, top-ranked differentially expressed gene (top-DEG), regularized, and wrapper panels and a permuted-label null. Panels were transferred to GSE30528 and GSE142025.