Show simple item record

dc.contributor.authorElmarakeby, Haitham Abdulrahmanen_US
dc.date.accessioned2018-12-07T07:00:49Z
dc.date.available2018-12-07T07:00:49Z
dc.date.issued2017-06-14
dc.identifier.othervt_gsexam:11742en_US
dc.identifier.urihttp://hdl.handle.net/10919/86264
dc.description.abstractThe last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome, and microbiome in different organisms are producing large amounts of high-dimensional data every day. High-dimensional data provides unprecedented challenges and opportunities to gain a better understanding of biological systems. Unlike other data types, biological data imposes more constraints on researchers. Biologists are not only interested in accurate predictive models that capture complex input-output relationships, but they also seek a deep understanding of these models. In the last few years, deep models have achieved better performance in computational prediction tasks compared to other approaches. Deep models have been extensively used in processing natural data, such as images, text, and recently sound. However, application of deep models in biology is limited. Here, I propose to use deep models for output prediction, dimension reduction, and feature selection of biological data to get better interpretation and understanding of biological systems. I demonstrate the applicability of deep models in a domain that has a high and direct impact on health care. In this research, novel deep learning models have been introduced to solve pressing biological problems. The research shows that deep models can be used to automatically extract features from raw inputs without the need to manually craft features. Deep models are used to reduce the dimensionality of the input space, which resulted in faster training. Deep models are shown to have better performance and less variant output when compared to other shallow models even when an ensemble of shallow models is used. Deep models are shown to be able to process non-classical inputs such as sequences. Deep models are shown to be able to naturally process input sequences to automatically extract useful features.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectMachine Learningen_US
dc.subjectComputational Biologyen_US
dc.subjectDeep Learningen_US
dc.subjectCanceren_US
dc.subjectDrug Responseen_US
dc.titleDeep Learning for Biological Problemsen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairHeath, Lenwood S.en_US
dc.contributor.committeememberZhang, Liqingen_US
dc.contributor.committeememberFeng, Wu-Chunen_US
dc.contributor.committeememberElHefnawi, Mahmoud M.en_US
dc.contributor.committeememberSheng, Zhien_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record