Please use this identifier to cite or link to this item: http://theses.iitj.ac.in:8080/jspui/handle/123456789/142
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dc.contributor.advisorBagler, Ganesh-
dc.date.accessioned2020-05-07T23:44:12Z-
dc.date.available2020-05-07T23:44:12Z-
dc.date.issued2017-06-
dc.identifier.citationKanji, Rakesh. (2017). Systems Modeling of Target and Chemical Profiles of Drugs to Predict Their Phenotypic Side Effects With Canonical Correlation Analysis (Doctor's thesis). Indian Institute of Technology Jodhpur, Jodhpur.en_US
dc.identifier.urihttp://theses.iitj.ac.in:8080/jspui/handle/123456789/142-
dc.description.abstractDespite technological advances and improved understanding of biological systems, drug discovery remains an inefficient and arduous task, with the high attrition of candidate molecules. Side effects (adverse reactions) is one of the key factors contributing to the rejection of candidate molecules with therapeutic potential. Hence, accurate prediction of phenotypic side effects is an important problem in drug discovery. The action of drugs needs to be seen from the systems perspective knowing that cellular mechanisms form a web of interactions with intricate cross-talks among biomolecules. Availability of data capturing molecular interaction of drugs, and their phenotypic side effects have facilitated systems-level models aimed at prediction of potential side effects. Towards the goal of predicting side effects, objectives set in this thesis were driven by the idea of creating holistic models using empirical data, and devising mathematical as well as computational strategies. We integrated data from existing resources such as DrugBank and SIDER for systems-level investigations of side effects, and developed an integrative Generalized Canonical Correlation Analysis model which facilitates consolidation of various drugs features. We concluded that models implementing chemical profiles show more consistent accuracy than those based on target profiles. Further we constructed a graph theoretical model of biological space to account for associations among drug targets, and by comparing the performance of various network metrics inferred that simple network parameters are comparable to intricate parameters. Our studies performed for identification of minimal ‘known side effects’ set as a predictor for a class of adverse reactions suggest that, partial information of side effects profile could be used as a factor for arriving at the remaining side effects. Finally, towards the goal of obtaining drug features that contribute the most to side effects prediction, we developed a partial canonical correlation analysis model that facilitates enumeration of contribution from individual drug features. Our systems-level investigations offer insights into mechanisms of adverse drug reactions and provide data-driven methods for their prediction.en_US
dc.description.statementofresponsibilityby Rakesh Kanjien_US
dc.format.extentxvi, 100p.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Technology Jodhpuren_US
dc.rightsIIT Jodhpuren_US
dc.subject.ddcSystems Modelingen_US
dc.subject.ddcTargeten_US
dc.subject.ddcChemicalen_US
dc.subject.ddcProfiles of Drugsen_US
dc.subject.ddcPhenotypic Side Effectsen_US
dc.subject.ddcCanonical Correlation Analysisen_US
dc.titleSystems Modeling of Target and Chemical Profiles of Drugs to Predict Their Phenotypic Side Effects With Canonical Correlation Analysisen_US
dc.typeThesisen_US
dc.creator.researcherKanji, Rakesh-
dc.date.registered2012-
dc.date.awarded2017-09-
dc.publisher.placeJodhpuren_US
dc.publisher.departmentBioscience and Bioengineeringen_US
dc.type.degreeDoctor of Philosophyen_US
dc.format.accompanyingmaterialCDen_US
dc.description.notecol. ill.; including bibliographyen_US
dc.identifier.accessionTP00019-
Appears in Collections:Ph. D. Theses

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01_title.pdf68.7 kBAdobe PDFView/Open
02_declaration.pdf56.14 kBAdobe PDFView/Open
03_certificate.pdf56.69 kBAdobe PDFView/Open
04_abstract.pdf44.39 kBAdobe PDFView/Open
05_acknowledgements.pdf69.16 kBAdobe PDFView/Open
06_contents.pdf57.86 kBAdobe PDFView/Open
07_list_of_figures.pdf147.78 kBAdobe PDFView/Open
08_list_of_tables.pdf204.94 kBAdobe PDFView/Open
09_list_of_symbols.pdf56.51 kBAdobe PDFView/Open
10_list_of_abbreviations.pdf39.7 kBAdobe PDFView/Open
11_chapter 1.pdf228.93 kBAdobe PDFView/Open
12_chapter 2.pdf250.64 kBAdobe PDFView/Open
13_chapter 3.pdf788.69 kBAdobe PDFView/Open
14_chapter 4.pdf707.67 kBAdobe PDFView/Open
15_chapter 5.pdf763.34 kBAdobe PDFView/Open
16_chapter 6.pdf806.14 kBAdobe PDFView/Open
17_chapter 7.pdf1.53 MBAdobe PDFView/Open
18_chapter 8.pdf916.99 kBAdobe PDFView/Open
19_chapter 9 summary.pdf115.27 kBAdobe PDFView/Open
20_annexure A.pdf200.81 kBAdobe PDFView/Open
21_annexure B.pdf253.5 kBAdobe PDFView/Open
22_references.pdf318.46 kBAdobe PDFView/Open


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