PT  - JOURNAL ARTICLE
AU  - Trinh-Trung-Duong Nguyen
AU  - Nguyen-Quoc-Khanh Le
AU  - Quang-Thai Ho
AU  - Dinh-Van Phan
AU  - Yu-Yen Ou
TI  - TNFPred: Identifying tumor necrosis factors using hybrid features based on word embeddings
AID  - 10.1101/860791
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 860791
4099  - http://biorxiv.org/content/early/2019/12/01/860791.short
4100  - http://biorxiv.org/content/early/2019/12/01/860791.full
AB  - Background Cytokines 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. In 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 Chou’s 5-step rules to build a reliable classification tool.Results With 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.Conclusions These 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.AACamino acid compositionAccaccuracyAUCArea Under The CurveDPCdipeptide compositionIFNinterferonILinterleukinkNNk-nearest neighborMCCMatthew’s correlation coefficientNGFneuron growth factorNLPnatural language processingPSSMposition specific scoring matrixRFrandom forestROCreceiver operating characteristicSensensitivitySpecspecificitySVMsupport vector machineTGF-btransforminggrowth factor bTNFtumor necrosis factorTPCtripeptide compositiont-SNEt-Distributed Stochastic Neighbor Embedding