Computational Learning Approaches to Data Analytics in Biomedical Applications - 1st Edition - ISBN: 9780128144824

Computational Learning Approaches to Data Analytics in Biomedical Applications

1st Edition

Authors: Khalid Al-Jabery Tayo Obafemi-Ajayi Gayla Olbricht Donald Wunsch
Hardcover ISBN: 9780128144824
Imprint: Academic Press
Published Date: 1st September 2019
Page Count: 220
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Description

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.

Key Features

  • 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

Readership

Researchers in biomedical engineering, data processing, and statistics

Table of Contents

1. Introduction
2. Data analysis and pre-processing
3-5. Clustering algorithms and applications
6-7. Supervised Learning approaches
8-9. Statistical analysis tools and techniques (WEKA, SAP-HANA, R/Bioconductor, and JMP)
10. Genomic data analysis
11. Evaluation Metrics and cluster validation
12. Clusters Visualization
13. Bio informatics tools in MATLAB and Python (PS: this chapter can be distributed among other chapters)

Details

No. of pages:
220
Language:
English
Copyright:
© Academic Press 2020
Published:
Imprint:
Academic Press
Hardcover ISBN:
9780128144824

About the Author

Khalid Al-Jabery

Khalid Al-Jabery is currently a PhD candidate and research assistant at Missouri S&T. He obtained 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). He has gained hands-on experience on applying a variety of computational tools to machine learning projects on power optimization, smart grid, and medical datasets.

Affiliations and Expertise

Deputy Cheif Engineer, department of information management at Basrah Oil company, in Basrah, Iraq

Tayo Obafemi-Ajayi

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.

Affiliations and Expertise

Assistant Professor, Electrical Engineering, Missouri State University, USA

Gayla Olbricht

Dr. Olbricht is an Assistant 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.

Affiliations and Expertise

Assistant Professor, Department of Mathematics and Statistics, Missouri University of Science and Technology, USA

Donald Wunsch

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.

Affiliations and Expertise

Director, Applied Computational Intelligence Laboratory, University of Missouri, USA

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