國立虎尾科技大學 |

Associative Pattern Recognition for Biological Regulation Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Associative Pattern Recognition for Biological Regulation Data./
作者:	Xiao, Yiou.
面頁冊數:	1 online resource (144 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355648218

Associative Pattern Recognition for Biological Regulation Data.
Xiao, Yiou.

Associative Pattern Recognition for Biological Regulation Data. - 1 online resource (144 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--Syracuse University, 2018.

Includes bibliographical references

In the last decade, bioinformatics data has been accumulated at an unprecedented rate, thanks to the advancement in sequencing technologies. Such rapid development poses both challenges and promising research topics. In this dissertation, we propose a series of associative pattern recognition algorithms in biological regulation studies. In particular, we emphasize efficiently recognizing associative patterns between genes, transcription factors, histone modifications and functional labels using heterogeneous data sources (numeric, sequences, time series data and textual labels).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355648218Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Associative Pattern Recognition for Biological Regulation Data.
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Advisers: Kishan G. Mehrotra; Chilukuri K. Mohan; Ramesh Raina.
502 $a Thesis (Ph.D.)--Syracuse University, 2018.
504 $a Includes bibliographical references
520 $a In the last decade, bioinformatics data has been accumulated at an unprecedented rate, thanks to the advancement in sequencing technologies. Such rapid development poses both challenges and promising research topics. In this dissertation, we propose a series of associative pattern recognition algorithms in biological regulation studies. In particular, we emphasize efficiently recognizing associative patterns between genes, transcription factors, histone modifications and functional labels using heterogeneous data sources (numeric, sequences, time series data and textual labels).
520 $a In protein-DNA associative pattern recognition, we introduce an efficient algorithm for affinity test by searching for over-represented DNA sequences using a hash function and modulo addition calculation. This substantially improves the efficiency of next generation sequencing data analysis. In gene regulatory network inference, we propose a framework for refining weak networks based on transcription factor binding sites, thus improved the precision of predicted edges by up to 52%. In histone modification code analysis, we propose an approach to genome-wide combinatorial pattern recognition for "histone code to function" associative pattern recognition, and achieved improvement by up to 38:1%. We also propose a novel shape based modification pattern analysis approach, using this to successfully predict sub-classes of genes in flowering-time category. We also propose a "combination to combination" associative pattern recognition, and achieved better performance compared against multi-label classification and bidirectional associative memory methods. Our proposed approaches recognize associative patterns from different types of data efficiently, and provides a useful toolbox for biological regulation analysis. This dissertation presents a road-map to associative patterns recognition at genome wide level.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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710 2 $a Syracuse University. $b Electrical Engineering and Computer Science. $3 1184964
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