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Machine learning in cancer genomics

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dc.contributor.author Gupta, Taejas
dc.contributor.author Gupta, Anubha (Advisor)
dc.date.accessioned 2019-10-09T09:17:54Z
dc.date.available 2019-10-09T09:17:54Z
dc.date.issued 2019-04-28
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/782
dc.description.abstract Cancer is caused by an increased amount of cell growth in an area due to alterations in protein synthesis caused by mutations in certain genes known as cancer driver genes. Determining the gene regulatory network for the genes involved in the pathway can enable us to identify the driver genes that are responsible for cancer so that drugs targeting such genes can be developed. Advancements in DNA microarray technologies have made time series gene expression level data available for further analysis to infer the underlying gene regulatory network. My goal in this project is to infer gene regulatory networks from time series gene expression datasets using machine learning approaches, with a focus on recurrent neural networks and iteratively reweighted least squares with decorrelation. This report covers the various research papers that I studied, the approaches that I took and the results that I obtained for the task of inferring the gene regulatory network from time series gene expression data. en_US
dc.language.iso en_US en_US
dc.publisher IIITD-Delhi en_US
dc.subject Gene regulatory networks en_US
dc.subject Machine learning en_US
dc.subject Recurrent neural networks en_US
dc.subject Iteratively reweighted least squares, decorrelation en_US
dc.title Machine learning in cancer genomics en_US
dc.type Other en_US

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