Chengen Zou
ABSTRACT
Protein sequence classification is a crucial task in the field of bioinformatics as it helps to reveal various types of properties of proteins. Machine learning and deep learning algorithms have great application value in the problem of protein classification prediction. In this study, we propose a novel approach for extracting sequence features based on N-Gram model. We then utilize this approach to train and evaluate machine learning and deep learning algorithms on the same dataset. The experimental results show that the Random Forest method based on N-Gram features significantly outperforms other types of algorithmic models on this dataset, with a further increase in accuracy.
- T. E. Thingholm, L. Rönnstrand, and P. A. Rosenberg. 2020. Why and how to investigate the role of protein phosphorylation in ZIP and ZnT zinc transporter activity and regulation. Cell. Mol. Life Sci. 77, 3085–3102.Google Scholar
Cross Ref
- Kevin Tipton and Robert R Wolfe. 2001. Exercise, protein metabolism, and muscle growth. In International Journal of Sport Nutrition and Exercise Metabolism, 11 (1), pp. 109-132.Google ScholarCross Ref
- Oscar A. Tarazona and Olivier Pourquié. 2020. Exploring the Influence of Cell Metabolism on Cell Fate through Protein Post-translational Modifications. In Developmental Cell, vol. 55, no. 2, pp. 123-136. DOI: https://doi.org/10.1016/j.devcel.2020.06.035.Google ScholarCross Ref
- Brian Kuhlman and Philip Bradley. 2019. Advances in protein structure prediction and design. Nat Rev Mol Cell Biol 20, 681–697.Google ScholarCross Ref
- Batta Mahesh. 2020. Machine learning algorithms-a review. In International Journal of Science and Research (IJSR), 9(1): 381-386. DOI: 10.21275/ART20203995.Google ScholarCross Ref
- Ernest Yeboah Boateng, Joseph Otoo, and Daniel A. Abaye. 2020. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. In Journal of Data Analysis and Information Processing, vol. 8, no. 4, pp. 341-357. DOI: 10.4236/jdaip.2020.84020.Google ScholarCross Ref
- Leo Breiman. 2001. Random forests. Machine Learning 45, 1 (2001), 5-32.Google ScholarDigital Library
- Aakash Parmar, Rakesh Katariya, and Vatsal Patel. 2019. A Review on Random Forest: An Ensemble Classifier. In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018, J. Hemanth, X. Fernando, P. Lafata, and Z. Baig (Eds.). Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-03146-6_86.Google ScholarCross Ref
- Saad Albawi, Tareq Abed Mohammed, and Saad Al-Zawi. 2017. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, pp. 1-6. DOI: 10.1109/ICEngTechnol.2017.8308186.Google ScholarCross Ref
- Justine Boulent, Samuel Foucher, Jérôme Théau, and Pierre-Luc St-Charles. 2019. Convolutional Neural Networks for the Automatic Identification of Plant Diseases. In Front. Plant Sci. 10:941. DOI: 10.3389/fpls.2019.00941.Google ScholarCross Ref
- Divyanshu Aggarwal and Yasha Hasija. 2022. A Review of Deep Learning Techniques for Protein Function Prediction. arXiv preprint arXiv:2211.09705.Google Scholar
- Muhammad Tahir, Saeed Anwar, Ajmal Mian, and Abdul Wahab Muzaffar. 2022. Deep localization of subcellular protein structures from fluorescence microscopy images. In Neural Computing and Applications: 1-14.Google Scholar
- Damiano Perri, Marco Simonetti, Andrea Lombardi, Noelia Faginas-Lago, and Osvaldo Gervasi. 2020. Binary Classification of Proteins by a Machine Learning Approach. In: Osvaldo Gervasi Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12255. Springer, Cham. https://doi.org/10.1007/978-3-030-58820-5_41.Google ScholarDigital Library
- Stephen K. Burley, Helen M. Berman, Gerard J. Kleywegt, John L. Markley, Haruki Nakamura, and Sameer Velankar. 2017. Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive. In Protein Crystallography. Methods in Molecular Biology, vol 1607. Humana Press, New York, NY. DOI: https://doi.org/10.1007/978-1-4939-7000-1_26.Google ScholarCross Ref
- Dan Ofer, Nadav Brandes, and Michal Linial. 2021. The language of proteins: NLP, machine learning & protein sequences. In Computational and Structural Biotechnology Journal, Volume 19, Pages 1750-1758. ISSN 2001-0370. DOI: https://doi.org/10.1016/j.csbj.2021.03.022.Google ScholarCross Ref
- Jurafsky, D., & Martin, J. H. 2023. N-gram Language Models. In Speech and Language Processing, pp. 129-157.Google Scholar
- Gérard Biau and Erwan Scornet. 2016. A random forest guided tour. In TEST 25, pp. 197–227. DOI: https://doi.org/10.1007/s11749-016-0481-7.Google ScholarCross Ref
- Yanjun Qi. 2012. Random Forest for Bioinformatics. In Ensemble Machine Learning. Springer, New York, NY. DOI: https://doi.org/10.1007/978-1-4419-9326-7_11.Google ScholarCross Ref
- Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou. 2022. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. In IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, 6999-7019. DOI: 10.1109/TNNLS.2021.3084827.Google ScholarCross Ref
- Lina Zhang, Chengjin Zhang, Rui Gao, and Runtao Yang. 2015. An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics. In International Journal of Molecular Sciences 16, no. 9: 21734-21758. DOI: https://doi.org/10.3390/ijms160921734.Google ScholarCross Ref
- Karamjeet Kaur, Anil Kumar Sagar, Sudeshna Chakraborty, and Manoj Kumar Gupta. 2022. Accelerating the Performance of Sequence Classification Using GPU Based Ensemble Learning with Extreme Gradient Boosting. In Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1613. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-12638-3_22.Google ScholarCross Ref
Index Terms
- Automatic Protein Sequences Classification Using Machine Learning Methods based on N-Gram Model
Recommendations
Protein Classification using Modified N-Gram and Skip-Gram Models: Extended Abstract
ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health InformaticsMachine Learning (ML)-based classification of protein characteristics from primary sequences is an important tool for exploring candidate proteins in targeted drug discovery, mutational analysis, and functional identification. However, ML feature ...
Classifying protein sequences using hydropathy blocks
The annotation of proteins can be achieved by classifying the protein of interest into a certain known protein family to induce its functional and structural features. This paper presents a new method for classifying protein sequences based upon the ...
Functional characterization of unknown protein sequences using Neuro-Fuzzy based machine learning approach and sequence augmented feature
Highlights- We propose Neuro-Fuzzy and machine leaning based subcellular localization model.
- Proposed model is used for functional characterization of unknown protein.
- Unknown proteins are filtered as bacterial protein using Neuro-Fuzzy ...
AbstractFunctional characterization of the Unknown Protein Sequence (UPS) is significant for biological research such as disease diagnosis and drug design. In this work Neuro-Fuzzy based machine learning framework is proposed with two levels of ...
Comments