HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes

BMC Bioinformatics

Research

Mohamed Hashem¹  Walaa Gad¹  Eman Ismail¹ 

[1] Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt;

关键词: Gene prediction;    Boosting techniques;    Gene ontology;    Ensemble learning;    Functional gene network;    Gene classification;   

DOI  :  10.1186/s12859-022-05099-7

 received in 2022-10-10, accepted in 2022-12-06,  发布年份 2022

来源: Springer

【 摘 要 】

PurposeAutism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease.MethodsHybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children’s nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model.ResultsThe proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers.ConclusionThe proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

12888_2022_4365_Article_IEq1.gif

12982_2022_119_Article_IEq92.gif

12982_2022_119_Article_IEq90.gif

Fig. 1

12982_2022_119_Article_IEq88.gif

12982_2022_119_Article_IEq87.gif

12982_2022_119_Article_IEq86.gif

12982_2022_119_Article_IEq231.gif

12982_2022_119_Article_IEq229.gif

Fig. 4

12982_2022_119_Article_IEq81.gif

12982_2022_119_Article_IEq79.gif

12982_2022_119_Article_IEq76.gif

12982_2022_119_Article_IEq222.gif

12982_2022_119_Article_IEq214.gif

12982_2022_119_Article_IEq207.gif

12982_2022_119_Article_IEq71.gif

12982_2022_119_Article_IEq67.gif

12982_2022_119_Article_IEq60.gif

12982_2022_119_Article_IEq198.gif

12982_2022_119_Article_IEq192.gif

12888_2022_4137_Article_IEq1.gif

Fig. 1