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Machine learning and mapping algorit...

Mississippi State University.

Machine learning and mapping algorithms applied to proteomics problems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
書名/作者:	Machine learning and mapping algorithms applied to proteomics problems.
作者:	Sanders, William Shane.
面頁冊數:	149 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3163.
Contained By:	Dissertation Abstracts International72-06B.
標題:	Biology, Molecular.
標題:	Biology, Bioinformatics.
標題:	Computer Science.
ISBN:	9781124589176
摘要、提要註:	Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which peptides will be seen can allow researchers to analyze to what extent a given protein is observable. Cell penetrating peptides can possibly be utilized to allow targeted small molecule delivery across cellular membranes and possibly serve a role as drug delivery peptides. Peptides and proteins identified through mass spectrometry can help refine computational gene models and improve structural genome annotations.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3450345

Machine learning and mapping algorithms applied to proteomics problems.
Sanders, William Shane.

Machine learning and mapping algorithms applied to proteomics problems. - 149 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3163.

Thesis (Ph.D.)--Mississippi State University, 2011.

Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which peptides will be seen can allow researchers to analyze to what extent a given protein is observable. Cell penetrating peptides can possibly be utilized to allow targeted small molecule delivery across cellular membranes and possibly serve a role as drug delivery peptides. Peptides and proteins identified through mass spectrometry can help refine computational gene models and improve structural genome annotations.

ISBN: 9781124589176Subjects--Topical Terms:

422925
Biology, Molecular.

Machine learning and mapping algorithms applied to proteomics problems.
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020 $a 9781124589176
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100 1 $a Sanders, William Shane. $3 475392
245 1 0 $a Machine learning and mapping algorithms applied to proteomics problems.
300 $a 149 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3163.
500 $a Adviser: Susan M. Bridges.
502 $a Thesis (Ph.D.)--Mississippi State University, 2011.
520 $a Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which peptides will be seen can allow researchers to analyze to what extent a given protein is observable. Cell penetrating peptides can possibly be utilized to allow targeted small molecule delivery across cellular membranes and possibly serve a role as drug delivery peptides. Peptides and proteins identified through mass spectrometry can help refine computational gene models and improve structural genome annotations.
590 $a School code: 0132.
650 4 $a Biology, Molecular. $3 422925
650 4 $a Biology, Bioinformatics. $3 423568
650 4 $a Computer Science. $3 423143
690 $a 0307
690 $a 0715
690 $a 0984
710 2 $a Mississippi State University. $b Biochemistry and Molecular Biology. $3 475393
773 0 $t Dissertation Abstracts International $g 72-06B.
790 1 0 $a Bridges, Susan M., $e advisor
790 1 0 $a Burgess, Shane C. $e committee member
790 1 0 $a Willeford, Kenneth O. $e committee member
790 1 0 $a Boyle, John A. $e committee member
790 1 0 $a Hansen, Eric A. $e committee member
790 $a 0132
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3450345

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