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Associative pattern recognition for biological regulation data

Associative pattern recognition for biological regulation data ACM SIGBio Record ASSOCIATIVE PATTERN RECOGNITION FOR BIOLOGICAL REGULATION DATA Yiou Xiao B.E. Nanjing University, 2009 M.S. Syracuse University, 2011 Phd DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Information Science and Engineering Abstract The value of data analysis has become unprecedentedly recognized in the last decade. Nowadays, the great potential of data is appreciated by people from various backgrounds. From public relation experts to strategy makers; from big companies in silicon valley to scientists in fundamental sciences, people are devoting tremendous amount of attention, money and time to exploring its value. According to EMBL the total size of bioinformatics databases has grown to 70 Petabytes in 2015 (10 9 MBytes); Twitter users generate 250 million posts every day. The difficulty of storing, processing and analyzing big data has been recognized for a long time. However, the term "big data" gained great attention in recent years because of the big advancement in technologies. New infrastructures such as MapReduce, HDFS, Hadoop, NoSQL databases and GPU computation as well as deep neural networks algorithms such as LSTM and CNN rekindled the enthusiasm. Associative patterns between sets of objects are of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGBioinformatics Record Association for Computing Machinery

Associative pattern recognition for biological regulation data

Xiao, Yiou
ACM SIGBioinformatics Record , Volume 7 (2) – Oct 2, 2017
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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2331-9291
DOI
10.1145/3148241.3148242
Publisher site
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Abstract

ACM SIGBio Record ASSOCIATIVE PATTERN RECOGNITION FOR BIOLOGICAL REGULATION DATA Yiou Xiao B.E. Nanjing University, 2009 M.S. Syracuse University, 2011 Phd DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Information Science and Engineering Abstract The value of data analysis has become unprecedentedly recognized in the last decade. Nowadays, the great potential of data is appreciated by people from various backgrounds. From public relation experts to strategy makers; from big companies in silicon valley to scientists in fundamental sciences, people are devoting tremendous amount of attention, money and time to exploring its value. According to EMBL the total size of bioinformatics databases has grown to 70 Petabytes in 2015 (10 9 MBytes); Twitter users generate 250 million posts every day. The difficulty of storing, processing and analyzing big data has been recognized for a long time. However, the term "big data" gained great attention in recent years because of the big advancement in technologies. New infrastructures such as MapReduce, HDFS, Hadoop, NoSQL databases and GPU computation as well as deep neural networks algorithms such as LSTM and CNN rekindled the enthusiasm. Associative patterns between sets of objects are of

Journal

ACM SIGBioinformatics Record – Association for Computing Machinery

Published: Oct 2, 2017

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