Optimizing Cancer Classification and Gene Discovery with an Adaptive Learning Search Algorithm for Microarray Analysis  

作　　者：Chiwen Qu Heng Yao Tingjiang Pan Zenghui Lu 

机构地区：[1]School of Humanities and Management,Youjiang Medical University for Nationalities,Baise,533000,China [2]School of Public Health,Youjiang Medical University for Nationalities,Baise,533000,China [3]Baise Healthy Meteorological Integration Innovation Center,Baise,533000,China [4]School of Languages and Cultures,Youjiang Medical University for Nationalities,Baise,533000,China

出　　处：《Journal of Bionic Engineering》2025年第2期901-930,共30页仿生工程学报(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Number 62341210);Natural Science Foundation of Guangxi Province(Grant Number:2025GXNSFHA069267);Science and Technology Development Plan for Baise City(Grant Number 20233654).

摘　　要：DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine.

关 键 词：Gene selection Learning search algorithm Gene expression data CLASSIFICATION 

分 类 号：R730.2[医药卫生—肿瘤]