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Pacific Symposium on Biocomputing

Yves A Lussier, Atul J Butte, Haiquan Li, Rong Chen, Jason H Moore
This paper summarizes the workshop content on how the integration of large biomolecular and clinical datasets can enhance the field of population health via translational informatics. Large volumes of data present diverse challenges for existing informatics technology, in terms of computational efficiency, modeling effectiveness, statistical computing, discovery algorithms, and heterogeneous data integration. While accumulating large 'omics measurements on subjects linked with their electronic record remains a challenge, this workshop focuses on non-trivial linkages between large clinical and biomolecular datasets...
2019: Pacific Symposium on Biocomputing
Graciela Gonzalez-Hernandez, Zhiyong Lu, Robert Leaman, Davy Weissenbacher, Mary Regina Boland, Yong Chen, Jingcheng Du, Juliane Fluck, Casey S Greene, John Holmes, Aditya Kashyap, Rikke Linnemann Nielsen, Zhengqing Ouyang, Sebastian Schaaf, Jaclyn N Taroni, Cui Tao, Yuping Zhang, Hongfang Liu
Precision medicine, an approach for disease treatment and prevention that considers "individual variability in genes, environment, and lifestyle" 1 was endorsed by the National Institutes of Health, aided by the presidential Precision Medicine Initiative (PMI), in 2016. PMI provided funding for cancer research and for building a national cohort of one million or more U.S. participants, now known as the "All of Us" Research Program, which aims to expand its impact to all diseases. PMI was the catalyst to a widespread effort around precision medicine, as evidenced by the more than 1000 grants funded by different NIH institutes in just the last two years...
2019: Pacific Symposium on Biocomputing
Joanne Berghout, Yves A Lussier, Francesca Vitali, Martha L Bulyk, Maricel G Kann, Jason H Moore
Identifying functional elements and predicting mechanistic insight from non-coding DNA and noncoding variation remains a challenge. Advances in genome-scale, high-throughput technology, however, have brought these answers closer within reach than ever, though there is still a need for new computational approaches to analysis and integration. This workshop aims to explore these resources and new computational methods applied to regulatory elements, chromatin interactions, non-protein-coding genes, and other non-coding DNA...
2019: Pacific Symposium on Biocomputing
Martin G Seneviratne, Michael G Kahn, Tina Hernandez-Boussard
The vision of precision medicine relies on the integration of large-scale clinical, molecular and environmental datasets. Data integration may be thought of along two axes: data fusion across institutions, and data fusion across modalities. Cross-institutional data sharing that maintains semantic integrity hinges on the adoption of data standards and a push toward ontology-driven integration. The goal should be the creation of query-able data repositories spanning primary and tertiary care providers, disease registries, research organizations etc...
2019: Pacific Symposium on Biocomputing
Megan Doerr, Shira Grayson, Sarah Moore, Christine Suver, John Wilbanks, Jennifer Wagner
The United States' All of Us Research Program is a longitudinal research initiative with ambitious national recruitment goals, including of populations traditionally underrepresented in biomedical research, many of whom have high geographic mobility. The program has a distributed infrastructure, with key programmatic resources spread across the US. Given its planned duration and geographic reach both in terms of recruitment and programmatic resources, a diversity of state and territory laws might apply to the program over time as well as to the determination of participants' rights...
2019: Pacific Symposium on Biocomputing
Kipp W Johnson, Jessica K De Freitas, Benjamin S Glicksberg, Jason R Bobe, Joel T Dudley
Anonymized electronic health records (EHR) are often used for biomedical research. One persistent concern with this type of research is the risk for re-identification of patients from their purportedly anonymized data. Here, we use the EHR of 731,850 de-identified patients to demonstrate that the average patient is unique from all others 98.4% of the time simply by examining what laboratory tests have been ordered for them. By the time a patient has visited the hospital on two separate days, they are unique in 72...
2019: Pacific Symposium on Biocomputing
Sean Simmons, Bonnie Berger, Cenk Sahinalp
The proliferation of sequencing technologies in biomedical research has raised many new privacy concerns. These include concerns over the publication of aggregate data at a genomic scale (e.g. minor allele frequencies, regression coefficients). Methods such as differential privacy can overcome these concerns by providing strong privacy guarantees, but come at the cost of greatly perturbing the results of the analysis of interest. Here we investigate an alternative approach for achieving privacy-preserving aggregate genomic data sharing without the high cost to accuracy of differentially private methods...
2019: Pacific Symposium on Biocomputing
Angela Gasdaska, Derek Friend, Rachel Chen, Jason Westra, Matthew Zawistowski, William Lindsey, Nathan Tintle
As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed...
2019: Pacific Symposium on Biocomputing
Gamze G├╝rsoy, Arif Harmanci, Haixu Tang, Erman Ayday, Steven E Brenner
High-throughput technologies for biological data acquisition are advancing at an increasing pace. Most prominently, the decreasing cost of DNA sequencing has led to an exponential growth of sequence information, including individual human genomes. This session of the 2019 Pacific Symposium on Biocomputing presents the distinctive privacy and ethical challenges related to the generation, storage, processing, study, and sharing of individuals' biological data generated by multitude of technologies including but not limited to genomics, proteomics, metagenomics, bioimaging, biosensors, and personal health trackers...
2019: Pacific Symposium on Biocomputing
Maxwell P Gold, Alexander LeNail, Ernest Fraenkel
When analyzing biological data, it can be helpful to consider gene sets, or predefined groups of biologically related genes. Methods exist for identifying gene sets that are differential between conditions, but large public datasets from consortium projects and single-cell RNA-Sequencing have opened the door for gene set analysis using more sophisticated machine learning techniques, such as autoencoders and variational autoencoders. We present shallow sparsely-connected autoencoders (SSCAs) and variational autoencoders (SSCVAs) as tools for projecting gene-level data onto gene sets...
2019: Pacific Symposium on Biocomputing
Qiwen Hu, Casey S Greene
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many genes in thousands or tens of thousands of cells, though some datasets now reach the million-cell mark. Projecting high-dimensional scRNA-seq data into a low dimensional space aids downstream analysis and data visualization. Many recent preprints accomplish this using variational autoencoders (VAE), generative models that learn underlying structure of data by compress it into a constrained, low dimensional space...
2019: Pacific Symposium on Biocomputing
Tongxin Wang, Travis Johnson, Jie Zhang, Kun Huang
Single-cell RNA sequencing (scRNA-seq) techniques have been very powerful in analyzing heterogeneous cell population and identifying cell types. Visualizing scRNA-seq data can help researchers effectively extract meaningful biological information and make new discoveries. While commonly used scRNA-seq visualization methods, such as t-SNE, are useful in detecting cell clusters, they often tear apart the intrinsic continuous structure in gene expression profiles. Topological Data Analysis (TDA) approaches like Mapper capture the shape of data by representing data as topological networks...
2019: Pacific Symposium on Biocomputing
Yang Chen, Yuping Zhang, Zhengqing Ouyang
Cell trajectory reconstruction based on single cell RNA sequencing is important for obtaining the landscape of different cell types and discovering cell fate transitions. Despite intense effort, analyzing massive single cell RNA-seq datasets is still challenging. We propose a new method named Landmark Isomap for Single-cell Analysis (LISA). LISA is an unsupervised approach to build cell trajectory and compute pseudo-time in the isometric embedding based on geodesic distances. The advantages of LISA include: (1) It utilizes k-nearest-neighbor graph and hierarchical clustering to identify cell clusters, peaks and valleys in low-dimension representation of the data; (2) Based on Landmark Isomap, it constructs the main geometric structure of cell lineages; (3) It projects cells to the edges of the main cell trajectory to generate the global pseudo-time...
2019: Pacific Symposium on Biocomputing
Lana X Garmire, Guo-Cheng Yuan, Rong Fan, Gene W Yeo, John Quackenbush
Single-cell genomics technology is an exciting emerging area that holds the promise to revolutionize our understanding of diseases and associated biological processes. It allows us to explore processes active in bulk tissue samples, survey tissue complexity, characterize heterogeneous cell populations and explore the role of cellular heterogeneity and interactions in disease. To deal with these new experimental data, new computational methods, software, and data portals to analyze, integrate and interpret the complexity of the system are clearly needed...
2019: Pacific Symposium on Biocomputing
Li-Fang Cheng, Niranjani Prasad, Barbara E Engelhardt
Laboratory testing is an integral tool in the management of patient care in hospitals, particularly in intensive care units (ICUs). There exists an inherent trade-off in the selection and timing of lab tests between considerations of the expected utility in clinical decision-making of a given test at a specific time, and the associated cost or risk it poses to the patient. In this work, we introduce a framework that learns policies for ordering lab tests which optimizes for this trade-off. Our approach uses batch off-policy reinforcement learning with a composite reward function based on clinical imperatives, applied to data that include examples of clinicians ordering labs for patients...
2019: Pacific Symposium on Biocomputing
Francesca Vitali, Joanne Berghout, Jungwei Fan, Jianrong Li, Qike Li, Haiquan Li, Yves A Lussier
Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions...
2019: Pacific Symposium on Biocomputing
Binglan Li, Yogasudha Veturi, Yuki Bradford, Shefali S Verma, Anurag Verma, Anastasia M Lucas, David W Haas, Marylyn D Ritchie
Transcriptome-wide association studies (TWAS) have recently gained great attention due to their ability to prioritize complex trait-associated genes and promote potential therapeutics development for complex human diseases. TWAS integrates genotypic data with expression quantitative trait loci (eQTLs) to predict genetically regulated gene expression components and associates predictions with a trait of interest. As such, TWAS can prioritize genes whose differential expressions contribute to the trait of interest and provide mechanistic explanation of complex trait(s)...
2019: Pacific Symposium on Biocomputing
Derek Reiman, Lingdao Sha, Irvin Ho, Timothy Tan, Denise Lau, Aly A Khan
Patient responses to cancer immunotherapy are shaped by their unique genomic landscape and tumor microenvironment. Clinical advances in immunotherapy are changing the treatment landscape by enhancing a patient's immune response to eliminate cancer cells. While this provides potentially beneficial treatment options for many patients, only a minority of these patients respond to immunotherapy. In this work, we examined RNA-seq data and digital pathology images from individual patient tumors to more accurately characterize the tumor-immune microenvironment...
2019: Pacific Symposium on Biocomputing
Xinyuan Zhang, Yogasudha Veturi, Shefali Verma, William Bone, Anurag Verma, Anastasia Lucas, Scott Hebbring, Joshua C Denny, Ian B Stanaway, Gail P Jarvik, David Crosslin, Eric B Larson, Laura Rasmussen-Torvik, Sarah A Pendergrass, Jordan W Smoller, Hakon Hakonarson, Patrick Sleiman, Chunhua Weng, David Fasel, Wei-Qi Wei, Iftikhar Kullo, Daniel Schaid, Wendy K Chung, Marylyn D Ritchie
The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time...
2019: Pacific Symposium on Biocomputing
Maya Varma, Kelley Marie Paskov, Jae-Yoon Jung, Brianna Sierra Chrisman, Nate Tyler Stockham, Peter Yigitcan Washington, Dennis Paul Wall
Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder affecting 1 in 59 children. While noncoding genetic variation has been shown to play a major role in many complex disorders, the contribution of these regions to ASD susceptibility remains unclear. Genetic analyses of ASD typically use unaffected family members as controls; however, we hypothesize that this method does not effectively elevate variant signal in the noncoding region due to family members having subclinical phenotypes arising from common genetic mechanisms...
2019: Pacific Symposium on Biocomputing
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