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Computational Learning Approaches to Data Analytics in Biomedical Applications

  • ID: 4622020
  • Book
  • November 2019
  • 310 Pages
  • Elsevier Science and Technology

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained.

  • Includes an overview of data analytics in biomedical applications and current challenges
  • Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices
  • Provides complete coverage of computational and statistical analysis tools for biomedical data analysis
  • Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

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1. Introduction 2. Data Preparation 3. Clustering Algorithms 4. Supervised learning 5. Statistical Analysis tools and techniques 6. Genomic Data Analysis 7. Evaluation Metrics 8. Visualization 9. Bio informatics tools in MATLAB and Python

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Al-Jabery, Khalid
Dr. Al-Jabery is a Deputy-Chief engineer in Barah Oil Company. He obtained his Ph.D. in Electrical and Computer engineering from Missouri S&T in 2018, his BS, and M.Sc. in Computer Engineering at the University of Basrah in Iraq in 2005 and 2009 respectively. He has more than 6 years of experience as an IT engineer. He worked for ExxonMobil, South Oil Company-Iraq, and International Organization of Migration (IOM). His research interests are Reinforcement learning, Clustering, Data analysis, Power optimization, and Artificial Neural network.
Obafemi-Ajayi, Tayo
Dr. Obafemi-Ajayi is an Assistant Professor of Electrical Engineering at Missouri State University (MSU) in the Engineering Program, a joint program with Missouri S&T. She completed a post-doctoral fellowship with the Applied Computational Intelligence Lab at S&T May 2016, working on clustering and genomic data analysis related to Autism. She obtained her PhD in Computer Science from Illinois Institute of Technology. Her research interests are machine learning, bioinformatics, and data mining.
Olbricht, Gayla
Dr. Olbricht is an Associate Professor in the Department of Mathematics and Statistics at Missouri S&T. She earned her Ph.D. in Statistics from Purdue University. Her research interests include Markov models, regression analysis, statistical genomics, and bioinformatics.
Wunsch, Donald
Dr. Wunsch is the Mary K. Finley Missouri Distinguished Professor, Missouri University of Science and Technology (Missouri S&T). He received his Ph.D. in Electrical Engineering from the University of Washington, Seattle. His research interests include clustering, adaptive resonance and reinforcement learning architectures (hardware and applications), bioinformatics. He is the author of nine books and over a dozen book chapters, including Neural Networks in Micromechanics from Springer and Clustering from Wiley IEEE Press.
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