Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction. (December 2022)

• Record Type: Journal Article
• Authors: He, Zhihui; Lin, Yingqing; Wei, Runguo; Liu, Cheng; Jiang, Dazhi
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: Repulsion -- Attraction -- Cancer driver gene -- Hybrid algorithm -- Data fusion; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106236
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset. Highlights: In spired by the search strategy with the capability of global search in EA, a gravitational kernel based repulsion search mechanism is proposed. Due to the repulsion search mechanism, there is an increase in the D-value of similarity score between these promising genes and ordinary genes, which is conducive to the search for these promising genes. A vital few-based attraction search mechanism is proposed according to the idea of elite individuals in EA. Through the attraction search mechanism, the candidate genes with similar mutation effects are searched for more efficiently by the driver genes similar to them through attraction effects. A genetic algorithm is adopted to optimize the SVM model with an organic fusion of gravitational kernel, mutation kernel and PPI kernel. In this way, the algorithm can learn more valid information.