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A predictive model for secondary RNA...

Koessler, Denise.

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A predictive model for secondary RNA structure using graph theory and a neural network.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A predictive model for secondary RNA structure using graph theory and a neural network./
作者:	Koessler, Denise.
面頁冊數:	55 p.
附註:	Source: Masters Abstracts International, Volume: 48-05, page: 3021.
Contained By:	Masters Abstracts International48-05.
標題:	Biology, Molecular. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1484815
ISBN:	9781124007953

A predictive model for secondary RNA structure using graph theory and a neural network.
Koessler, Denise.

A predictive model for secondary RNA structure using graph theory and a neural network. - 55 p.

Source: Masters Abstracts International, Volume: 48-05, page: 3021.

Thesis (M.S.)--East Tennessee State University, 2010.

In this work we use a graph-theoretic representation of secondary RNA structure found in the database RAG: RNA-As-Graphs. We model the bonding of two RNA secondary structures to form a larger structure with a graph operation called merge. The resulting data from each tree merge operation is summarized and represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not based on the merge data vector.

ISBN: 9781124007953Subjects--Topical Terms:

1017719
Biology, Molecular.

A predictive model for secondary RNA structure using graph theory and a neural network.
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245 1 2 $a A predictive model for secondary RNA structure using graph theory and a neural network.
300 $a 55 p.
500 $a Source: Masters Abstracts International, Volume: 48-05, page: 3021.
500 $a Advisers: Teresa Haynes; Debra Knisley.
502 $a Thesis (M.S.)--East Tennessee State University, 2010.
520 $a In this work we use a graph-theoretic representation of secondary RNA structure found in the database RAG: RNA-As-Graphs. We model the bonding of two RNA secondary structures to form a larger structure with a graph operation called merge. The resulting data from each tree merge operation is summarized and represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not based on the merge data vector.
520 $a The network correctly assigned a high probability of RNA-likeness to trees identified as RNA-like in the RAG database, and a low probability of RNA-likeness to those classified as not RNA-like in the RAG database. We then used the neural network to predict the RNA-likeness of all the trees of order 9. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel.
590 $a School code: 0069.
650 4 $a Biology, Molecular. $3 1017719
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Mathematics. $3 515831
650 4 $a Biology, Bioinformatics. $3 1018415
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790 1 0 $a Haynes, Teresa, $e advisor
790 1 0 $a Knisley, Debra, $e advisor
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791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1484815