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Open Access Publications from the University of California

Machine Learning for High Throughput Genomic Data Analysis

  • Author(s): Li, Yi
  • Advisor(s): Xie, Xiaohui
  • et al.
Abstract

Machine learning methods have been successfully applied to computational biology and bioinformatics for decades with both unsupervised learning and supervised learning. Recent advancement in high throughput genomic data profiling, such as high throughput sequencing and large-scale gene expression profiling, has became a powerful tool for both fundamental biological research and medicine. For example, high throughput sequencing now is possible to sequence billions of bases both fast and cheap, such as Illumina's latest sequencer HiSeq X that can sequence 32 human genomes per week with each costing less than \$1000. With the generation of millions or even billions of signals (e.g. sequencing reads) per experiment and thousands or even millions of experiments per study (e.g. large-scale gene expression profiling), there arises a great need for more advanced machine learning models for analysing high throughput genomic data using both unsupervised and supervised learning methods. In this thesis, we try to solve two main challenges in high throughput genomic data analysis, 1) deconvolving the sequencing data from more than one cell population, e.g. heterogeneous tumor tissues, using unsupervised probabilistic learning methods such as mixture models with latent variables; 2) modelling the nonlinear and hierarchical patterns within high throughput genomic data using supervised deep learning methods such as convolutional neural networks. We present five new models to solve these two challenges, each of them is applied to a specific problem. The first three models focus on deconvolving tumor heterogeneity: Chapter 2 presents a probabilistic model to deconvolve tumor purity and ploidy; Chapter 3 further extends the model to infer tumor subclonal populations; Chapter 4 presents a probabilistic model to deconvolve tumor transcriptome expression. The last two models focus on applying deep learning methods in analysing large scale genomic data: Chapter 5 presents a deep learning method for gene expression inference; Chapter 6 presents a deep learning method to understand sequence conservation.

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