biostudies-literatureUnknown6(6)Chuang LY<h4>Background</h4>Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5' end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed.<h4>Methodology/principal findings</h4>We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave rPloS onee21036https://www.ebi.ac.uk/biostudies/studies/S-EPMC3125183biostudies-literatureParticle swarm optimization with reinforcement learning for the prediction of CpG islands in the human genome.PMC3125183Yang CHChuang LYHuang HCLin MCfalseParticle swarm optimization with reinforcement learning for the prediction of CpG islands in the human genome.<h4>Background</h4>Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5' end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed.<h4>Methodology/principal findings</h4>We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave r2011-01-01T00:00:00Z20112025-07-11T03:04:05.348Z2025-07-11T03:04:05.348ZS-EPMC31251832173860210.1371/journal.pone.0021036