MACHINE LEARNING IN BIOINFORMATICS OF PROTEIN SEQUENCES

Machine Learning in Bioinformatics of Protein Sequences guides readers around the rapidly advancing world of cutting-edge machine learning applications in the protein bioinformatics field. Edited by bioinformatics expert, Dr Lukasz Kurgan, and with contributions by a dozen of accomplished researchers, this book provides a holistic view of the structural bioinformatics by covering a broad spectrum of algorithms, databases and software resources for the efficient and accurate prediction and characterization of functional and structural aspects of proteins. It spotlights key advances which include deep neural networks, natural language processing-based sequence embedding and covers a wide range of predictions which comprise of tertiary structure, secondary structure, residue contacts, intrinsic disorder, protein, peptide and nucleic acids-binding sites, hotspots, post-translational modification sites, and protein function. This volume is loaded with practical information that identifies and describes leading predictive tools, useful databases, webservers, and modern software platforms for the development of novel predictive tools.

Contents:

• Machine Learning Algorithms:
  • Deep Learning Techniques for De novo Protein Structure Prediction (Chunqiu Xia and Hong-Bin Shen)
• Inputs for Machine Learning Models:
  • Application of Sequence Embedding in Protein Sequence-Based Predictions (Nabil Ibtehaz and Daisuke Kihara)
  • Applications of Natural Language Processing Techniques in Protein Structure and Function Prediction (Bin Liu, Ke Yan, Yi-He Pang, Jun Zhang, Jiang-Yi Shao, Yi-Jun Tang and Ning Wang)
  • NLP-based Encoding Techniques for Prediction of Post-translational Modification Sites and Protein Functions (Suresh Pokharel, Evgenii Sidorov, Doina Caragea and Dukka B KC)
  • Feature-Engineering from Protein Sequences to Predict Interaction Sites Using Machine Learning (Dana Mary Varghese, Ajay Arya and Shandar Ahmad)
• Predictors of Protein Structure and Function:
  • Machine Learning Methods for Predicting Protein Contacts (Shuaa M A Alharbi and Liam J McGuffin)
  • Machine Learning for Protein Inter-Residue Interaction Prediction (Yang Li, Yan Liu and Dong-Jun Yu)
  • Machine Learning for Intrinsic Disorder Prediction (Bi Zhao and Lukasz Kurgan)
  • Sequence-Based Predictions of Residues that Bind Proteins and Peptides (Qianmu Yuan and Yuedong Yang)
  • Machine Learning Methods for Predicting Protein-Nucleic Acids Interactions (Min Li, Fuhao Zhang and Lukasz Kurgan)
  • Identification of Cancer Hotspot Residues and Driver Mutations Using Machine Learning (Medha Pandey, P Anoosha, Dhanusha Yesudhas and M Michael Gromiha)
• Practical Resources:
  • Designing Effective Predictors of Protein Post-Translational Modifications Using iLearnPlus (Zhen Chen, Fuyi Li, Xiaoyu Wang, Yanan Wang, Lukasz Kurgan and Jiangning Song)
  • Databases of Protein Structure and Function Predictions at the Amino Acid Level (Bi Zhao and Lukasz Kurgan)

Readership: Graduate students and researchers in computational biology, bioinformatics, structural biology and computer science areas.

Key Features:

• Provides in-depth information across two impactful technology fields: machine learning and bioinformatics
• Focuses on cutting-edge topics including deep learning, intrinsic disorder and protein function prediction
• Written by an international group of accomplished bioinformaticians