title: Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.
abstract: Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues's method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues's method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.
authors: Department of Computer Science and Information Engineering, National Formosa University, 64 Wen-Hwa Road, Huwei, Yunlin 63205, Taiwan.
authors: Chien-Hung
authors: 0000-0003-3905-8915
authors: CH
authors: Huang
authors: Department of Computer Science and Information Engineering, National Formosa University, 64 Wen-Hwa Road, Huwei, Yunlin 63205, Taiwan.
authors: Huai-Shun
authors: 0000-0001-8498-0939
authors: HS
authors: Peng
authors: Department of Biomedical Informatics, Asia University, Wufeng Shiang, Taichung 41354, Taiwan ; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan.
authors: Ka-Lok
authors: 0000-0001-6315-6326
authors: KL
authors: Ng
mesh_terms: D000465:Algorithms; D000818:Animals; D030562:Databases, Protein; D006801:Humans; D000069550:Machine Learning; D009363:Neoplasm Proteins / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics*; D017434:Protein Structure, Tertiary; D020539:Sequence Analysis, Protein
chemical_list: D009363:Neoplasm Proteins