k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

• Published in: Frontiers in genetics 2019-02, Vol.10, p.33
• Main Authors: Xu, Lei, Liang, Guangmin, Liao, Changrui, Chen, Gin-Den, Chang, Chi-Chang
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; gene coding; Genetics; n-gram model; random forest; sequence information
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2019.00033

In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results.