TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings

BMC Medical Genomics

Yu-Yen Ou¹  Quang-Thai Ho¹  Trinh-Trung-Duong Nguyen¹  Nguyen-Quoc-Khanh Le²  Dinh-Van Phan³ 

[1] Department of Computer Science and Engineering, Yuan Ze University, 32003, Taoyuan, Taiwan;Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 106, Taipei City, Taiwan;Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 106, Taipei City, Taiwan;University of Economics, The University of Danang, 550000, Danang, Vietnam;

关键词: Machine learning;    Binary classification;    Natural language processing;    Feature extraction;   

DOI  :  10.1186/s12920-020-00779-w

来源: Springer

【 摘 要 】

BackgroundCytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes including immunity regulation, hematopoiesis, and inflammation. As one important family of cytokines, tumor necrosis factors have association with the regulation of a various biological processes such as proliferation and differentiation of cells, apoptosis, lipid metabolism, and coagulation. The implication of these cytokines can also be seen in various diseases such as insulin resistance, autoimmune diseases, and cancer. Considering the interdependence between this kind of cytokine and others, classifying tumor necrosis factors from other cytokines is a challenge for biological scientists.MethodsIn this research, we employed a word embedding technique to create hybrid features which was proved to efficiently identify tumor necrosis factors given cytokine sequences. We segmented each protein sequence into protein words and created corresponding word embedding for each word. Then, word embedding-based vector for each sequence was created and input into machine learning classification models. When extracting feature sets, we not only diversified segmentation sizes of protein sequence but also conducted different combinations among split grams to find the best features which generated the optimal prediction. Furthermore, our methodology follows a well-defined procedure to build a reliable classification tool.ResultsWith our proposed hybrid features, prediction models obtain more promising performance compared to seven prominent sequenced-based feature kinds. Results from 10 independent runs on the surveyed dataset show that on an average, our optimal models obtain an area under the curve of 0.984 and 0.998 on 5-fold cross-validation and independent test, respectively.ConclusionsThese results show that biologists can use our model to identify tumor necrosis factors from other cytokines efficiently. Moreover, this study proves that natural language processing techniques can be applied reasonably to help biologists solve bioinformatics problems efficiently.

【 授权许可】

CC BY