Deep Learning for Biological Problems
| Autor: | Elmarakeby, Haitham Abdulrahman |
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| Rok vydání: | 2017 |
| Předmět: | |
| Druh dokumentu: | Dissertation |
| Popis: | The 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. Ph. D. The world is generating more data than any time before. The abundance of data provides a great challenge and opportunity to get a better understanding of complex biological systems. The complexity of biological systems mandates better computational models that can make use the different types and formats of biological data. 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. In this research, I show that deep learning can be applied to solve different biological problems that are directly related to human health. In this research, deep learning is used to predict which genes are essential for cancer cell survival. Deep learning is used to predict which drug combinations can work together to better treat cancer. Deep learning is used to predict whether two proteins are interacting with each other. This can be helpful for example in finding potential targets of viral proteins inside the human body. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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