Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disea

Quantum computing at the frontiers of biological sciences

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Motivation: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. Results: Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype-phenotype association in whole genome-sized data

Evaluating Methods for Privacy-Preserving Data Sharing in Genomics

The availability of genomic data is often essential to progress in biomedical re- search, personalized medicine, drug development, etc. However, its extreme sensitivity makes it problematic, if not outright impossible, to publish or share it. In this dissertation, we study and build systems that are geared towards privacy preserving genomic data sharing. We first look at the Matchmaker Exchange, a platform that connects multiple distributed databases through an API and allows researchers to query for genetic variants in other databases through the network. However, queries are broadcast to all researchers that made a similar query in any of the connected databases, which can lead to a reluctance to use the platform, due to loss of privacy or competitive advantage. In order to overcome this reluctance, we propose a framework to support anonymous querying on the platform. Since genomic data’s sensitivity does not degrade over time, we analyze the real-world guarantees provided by the only tool available for long term genomic data storage. We find that the system offers low security when the adversary has access to side information, and we support our claims by empirical evidence. We also study the viability of synthetic data for privacy preserving data sharing. Since for genomic data research, the utility of the data provided is of the utmost importance, we first perform a utility evaluation on generative models for different types of datasets (i.e., financial data, images, and locations). Then, we propose a privacy evaluation framework for synthetic data. We then perform a measurement study assessing state-of-the-art generative models specifically geared for human genomic data, looking at both utility and privacy perspectives. Overall, we find that there is no single approach for generating synthetic data that performs well across the board from both utility and privacy perspectives

Partition-based Model Representation Learning

Modern machine learning consists of both task forces from classical statistics and modern computation. On the one hand, this field becomes rich and quick-growing; on the other hand, different convention from different schools becomes harder and harder to communicate over time. A lot of the times, the problem is not about who is absolutely right or wrong, but about from which angle that one should approach the problem. This is the moment when we feel there should be a unifying machine learning framework that can withhold different schools under the same umbrella. So we propose one of such a framework and call it ``representation learning''. Representations are for the data, which is almost identical to a statistical model. However, philosophically, we would like to distinguish from classical statistical modeling such that (1) representations are interpretable to the scientist, (2) representations convey the pre-existing subject view that the scientist has towards his/her data before seeing it (in other words, representations may not align with the true data generating process), and (3) representations are task-oriented. To build such a representation, we propose to use partition-based models. Partition-based models are easy to interpret and useful for figuring out the interactions between variables. However, the major challenge lies in the computation, since the partition numbers can grow exponentially with respect to the number of variables. To solve the problem, we need a model/representation selection method over different partition models. We proposed to use I-Score with backward dropping algorithm to achieve the goal. In this work, we explore the connection between the I-Score variable selection methodology to other existing methods and extend the idea into developing other objective functions that can be used in other applications. We apply our ideas to analyze three datasets, one is the genome-wide association study (GWAS), one is the New York City Vision Zero, and, lastly, the MNIST handwritten digit database. On these applications, we showed the potential of the interpretability of the representations can be useful in practice and provide practitioners with much more intuitions in explaining their results. Also, we showed a novel way to look at causal inference problems from the view of partition-based models. We hope this work serve as an initiative for people to start thinking about approaching problems from a different angle and to involve interpretability into the consideration when building a model so that it can be easier to be used to communicate with people from other fields