Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare

Baoying Wang (Waynesburg University, USA), Ruowang Li (Pennsylvania State University, USA) and William Perrizo (North Dakota State University, USA)
Indexed In: SCOPUS
Release Date: October, 2014|Copyright: © 2015 |Pages: 528
ISBN13: 9781466666115|ISBN10: 1466666110|EISBN13: 9781466666122|DOI: 10.4018/978-1-4666-6611-5

Description

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.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Database Management
  • Genomics
  • Ontologies Construction
  • Protein/RNA structure prediction
  • Proteomics
  • Scalability and Efficiency
  • Search Architectures
  • System Biology and Pathways

Reviews and Testimonials

While emerging technology has made data entry much easier, it also presents a unique set of challenges for researchers and medical professionals. Data sets have grown so large that extracting and analyzing data with old methods has become challenging. However, these data sets also provide an exciting opportunity to understand large scale patterns and make predictions about health care. This book is a complete reference to new information processing technology that combines ideas from biology, chemistry and medical science in order to productively manage electronic medical records. Specific topics covered include database management, genomics, proteomics and scalability, among others.

– ProtoView Book Abstracts (formerly Book News, Inc.)

Table of Contents and List of Contributors

Search this Book:
Reset

Author(s)/Editor(s) Biography

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.

Indices