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SVM and a novel POOL method coupled ...

Northeastern University., Computer and Information Science.

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SVM and a novel POOL method coupled with THEMATICS for protein active site prediction.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	SVM and a novel POOL method coupled with THEMATICS for protein active site prediction./
作者:	Tong, Wenxu.
面頁冊數:	164 p.
附註:	Advisers: Ronald J. Williams; Mary J. Ondrechen.
Contained By:	Dissertation Abstracts International69-02B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3297555
ISBN:	9780549472049

SVM and a novel POOL method coupled with THEMATICS for protein active site prediction.
Tong, Wenxu.

SVM and a novel POOL method coupled with THEMATICS for protein active site prediction. - 164 p.

Advisers: Ronald J. Williams; Mary J. Ondrechen.

Thesis (Ph.D.)--Northeastern University, 2008.

Protein active site prediction is a very important problem in bioinformatics. THEMATICS is a simple and effective method based on the special electrostatic properties of ionizable residues to predict such sites from protein three-dimensional structure alone. The process involves distinguishing computed titration curves with perturbed shape from normal ones; the differences are subtle in many cases. In this dissertation, I develop and apply special machine learning techniques to automate the process and achieve higher sensitivity than results from other methods while maintaining high specificity. I first present application of support vector machines (SVM) to automate the active site prediction using THEMATICS; at the time this work was developed, it achieved better performance than any other 3D structure based methods. I then present the more recently developed Partial Order Optimal Likelihood (POOL) method, which estimates the probabilities of residues being active under certain natural monotonicity assumptions. The dissertation shows that applying the POOL method just on THEMATICS features outperforms the SVM results. Furthermore, since the overall approach is based on estimating certain probabilities from labeled training data, it provides a principled way to combine the use of THEMATICS features with other non-electrostatic features proposed by others. In particular, I consider the use of geometric features as well, and the resulting classifiers are the best structure-only predictors yet found. Finally, I show that adding in sequence-based conservation scores where applicable yields a method that outperforms all existing method while using only whatever combination of structure-based or sequence-based features is available.

ISBN: 9780549472049Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

SVM and a novel POOL method coupled with THEMATICS for protein active site prediction.
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502 $a Thesis (Ph.D.)--Northeastern University, 2008.
520 $a Protein active site prediction is a very important problem in bioinformatics. THEMATICS is a simple and effective method based on the special electrostatic properties of ionizable residues to predict such sites from protein three-dimensional structure alone. The process involves distinguishing computed titration curves with perturbed shape from normal ones; the differences are subtle in many cases. In this dissertation, I develop and apply special machine learning techniques to automate the process and achieve higher sensitivity than results from other methods while maintaining high specificity. I first present application of support vector machines (SVM) to automate the active site prediction using THEMATICS; at the time this work was developed, it achieved better performance than any other 3D structure based methods. I then present the more recently developed Partial Order Optimal Likelihood (POOL) method, which estimates the probabilities of residues being active under certain natural monotonicity assumptions. The dissertation shows that applying the POOL method just on THEMATICS features outperforms the SVM results. Furthermore, since the overall approach is based on estimating certain probabilities from labeled training data, it provides a principled way to combine the use of THEMATICS features with other non-electrostatic features proposed by others. In particular, I consider the use of geometric features as well, and the resulting classifiers are the best structure-only predictors yet found. Finally, I show that adding in sequence-based conservation scores where applicable yields a method that outperforms all existing method while using only whatever combination of structure-based or sequence-based features is available.
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