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
Researchers in biomedical engineering, data processing, and statistics
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)
- No. of pages:
- © Academic Press 2020
- 1st September 2019
- Academic Press
- Hardcover ISBN:
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.
Deputy Cheif Engineer, department of information management at Basrah Oil company, in Basrah, Iraq
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.
Assistant Professor, Electrical Engineering, Missouri State University, USA
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.
Assistant Professor, Department of Mathematics and Statistics, Missouri University of Science and Technology, USA
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.
Director, Applied Computational Intelligence Laboratory, University of Missouri, USA