Big Data Analytics in Bioinformatics and Healthcare
- 13h 37m
- Baoying Wang, Ruowang Li, William Perrizo (eds)
- IGI Global
- 2015
As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information.
Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.
About the Editors
Baoying Wang is an associate professor in Waynesburg University. She received her PhD degree in Computer Science from North Dakota State University, Master’s degree from Minnesota State University of St. Cloud, and Bachelor’s degree from Beijing University of Science and Technology. Her research interests include data mining, data warehouse, bioinformatics, parallel computing. She is a member of ACM, ISCA, and SIGMOD. As professional activities, she serves as a reviewer and/or a committee member of many international conferences and journals.
Ruowang Li is pursuing a PhD in Bioinformatics and Genomics at the Pennsylvania State University, University Park, Pennsylvania, USA. He was fascinated by the complexity of the molecular biology, so he studied Biology and Computer Science at Worcester Polytechnic Institute, Worcester, Massachusetts, USA, from 2007 to 2011. His has been developing and applying computational methods to identify the molecular factors affecting individuals’ chemotherapeutic drug responses as well as cancer patients’ survival status. He is currently a National Science Foundation graduate fellow in the laboratory of Dr. Marylyn Ritchie.
William Perrizo is a Professor of Computer Science at North Dakota State University. He holds a PhD degree from the University of Minnesota, a Master’s degree from the University of Wisconsin, and a Bachelor’s degree from St. John's University. He has been a Research Scientist at the IBM Advanced Business Systems Division and the U.S. Air Force Electronic Systems Division. His areas of expertise are Data Mining, Knowledge Discovery, Database Systems, Distributed Database Systems, High Speed Computer and Communications Networks, Precision Agriculture, and Bioinformatics. He is a member of ISCA, ACM, IEEE, IAAA, and AAAS.
In this Book
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Advanced Datamining Using RNAseq Data
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Text Mining on Big and Complex Biomedical Literature
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Interactive Data Visualization Techniques Applied to Healthcare Decision Making
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Large-Scale Regulatory Network Analysis from Microarray Data: Application to Seed Biology
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Detection and Employment of Biological Sequence Motifs
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Observer-Biased Analysis of Gene Expression Profiles
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Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and its Application to Bioinformatics
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The Role of Big Data in Radiation Oncology: Challenges and Potentials
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Analysis of Genomic Data in a Cloud Computing Environment
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Pathway Analysis and its Applications
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Computational Systems Biology Perspective on Tuberculosis in Big Data Era: Challenges and Future Goals
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Bioinformatics-Driven Big Data Analytics in Microbial Research
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Perspectives on Data Integration in Human Complex Disease Analysis
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Current Study Designs, Methods, and Future Directions of Genetic Association Mapping
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Personalized Disease Phenotypes from Massive OMICs Data
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Intellectual Property Protection for Synthetic Biology, Including Bioinformatics and Computational Intelligence
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Clinical Data Linkages in Spinal Cord Injuries (SCI) in Australia: What Are the Concerns?
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The Benefits of Big Data Analytics in the Healthcare Sector: What Are they and Who Benefits?
