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Development of novel unsupervised an...

Mohiddin, Syed Basha.

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Development of novel unsupervised and supervised informatics methods for drug discovery applications.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Development of novel unsupervised and supervised informatics methods for drug discovery applications./
作者:	Mohiddin, Syed Basha.
面頁冊數:	199 p.
附註:	Adviser: James F. Rathman.
Contained By:	Dissertation Abstracts International67-01B.
標題:	Engineering, Chemical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3205277
ISBN:	9780542512285

Development of novel unsupervised and supervised informatics methods for drug discovery applications.
Mohiddin, Syed Basha.

Development of novel unsupervised and supervised informatics methods for drug discovery applications. - 199 p.

Adviser: James F. Rathman.

Thesis (Ph.D.)--The Ohio State University, 2006.

As of 2002, the cost of discovering a new drug was nearly

ISBN: 9780542512285Subjects--Topical Terms:

1018531
Engineering, Chemical.

Development of novel unsupervised and supervised informatics methods for drug discovery applications.
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100 1 $a Mohiddin, Syed Basha. $3 1287104
245 1 0 $a Development of novel unsupervised and supervised informatics methods for drug discovery applications.
300 $a 199 p.
500 $a Adviser: James F. Rathman.
500 $a Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0398.
502 $a Thesis (Ph.D.)--The Ohio State University, 2006.
520 $a As of 2002, the cost of discovering a new drug was nearly $8 02 million with a timeline of nearly 13.6 years. Despite the large investments in time and money, drugs that were successfully introduced in the market had to be withdrawn later due to efficacy (38%) and safety (20%) reasons. Improving the success rate in drug discovery is linked with two key steps in the process. First, in order to improve efficacy, there is a need for improved understanding of genetic biomarkers (targets for drug action) that are responsible for characterizing a given disease. Second, it is possible to improve drug safety, by predicting the activity/toxicity of potential drug candidates at an early stage prior to the initiation of expensive clinical trials. In this work, we develop a novel unsupervised informatics methodology that addresses characterization of both biological and chemical samples and identification of underlying key non-redundant features responsible for characterization. Biological samples are characterized into different groups (e.g. cancer types) based on gene expression profiling and the genetic biomarkers most responsible for characterization are identified. Similarly, chemical compounds are characterized into different groups with varying activity/toxicity based on structural, physical and chemical property data of the chemical compounds.
520 $a The methodology developed in this work relies largely on the multivariate aspects of principal component analysis and the application of k means clustering algorithm in a hierarchically recursive manner to achieve unsupervised multi-class classification. The principal components are replaced by the corresponding partial least square (PLS) components in the supervised scenario. Selection of influential components (principal components in unsupervised case and PLS components in supervised case) for the purpose of classification is demonstrated and is one of the key steps for the success of this methodology. Hierarchical k-means is applied recursively to achieve binary classification at each stage eventually resulting in multi-class classification. Identification of features responsible for classification is achieved by examining the appropriate loadings of the principal or PLS components along with their coefficient of correlation with influential components.
590 $a School code: 0168.
650 4 $a Engineering, Chemical. $3 1018531
650 4 $a Health Sciences, Pharmacology. $3 1017717
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792 $a 2006
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