Use Chou's 5-Steps Rule with Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

Trinh Trung Duong Nguyen, Quang Thai Ho, Nguyen Quoc Khanh Le, Van Dinh Phan, Yu Yen Ou

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

Original languageEnglish
Pages (from-to)1235-1244
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number2
Publication statusPublished - 2022


  • Electron transport protein
  • machine learning
  • natural language processing
  • support vector machine
  • word embedding

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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