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Pacific Symposium on Biocomputing

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https://www.readbyqxmd.com/read/29218922/methods-for-examining-data-quality-in-healthcare-integrated-data-repositories
#1
Vojtech Huser, Michael G Kahn, Jeffrey S Brown, Ramkiran Gouripeddi
This paper summarizes content of the workshop focused on data quality. The first speaker (VH) described data quality infrastructure and data quality evaluation methods currently in place within the Observational Data Science and Informatics (OHDSI) consortium. The speaker described in detail a data quality tool called Achilles Heel and latest development for extending this tool. Interim results of an ongoing Data Quality study within the OHDSI consortium were also presented. The second speaker (MK) described lessons learned and new data quality checks developed by the PEDsNet pediatric research network...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218921/machine-learning-and-deep-analytics-for-biocomputing-call-for-better-explainability
#2
Dragutin Petkovic, Lester Kobzik, Christopher Re
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use information explaining why and how the MLDA approach made its decisions. We believe that much greater effort is needed to address the issue of MLDA explainability because of: 1) the ever increasing use and dependence on MLDA in biocomputing including the need for increased adoption by non-MLD experts; 2) the diversity, complexity and scale of biocomputing data and MLDA algorithms; 3) the emerging importance of MLDA-based decisions in patient care, in daily research, as well as in the development of new costly medical procedures and drugs...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218920/integrating-community-level-data-resources-for-precision-medicine-research
#3
William S Bush, Dana C Crawford, Farren Briggs, Darcy Freedman, Chantel Sloan
Precision Medicine focuses on collecting and using individual-level data to improve healthcare outcomes. To date, research efforts have been motivated by molecular-scale measurements, such as incorporating genomic data into clinical use. In many cases however, environmental, social, and economic factors are much more predictive of health outcomes, yet are not systematically used in clinical practice due to the difficulties in measurement and quantification. Advances in both the availability of electronic health information, environmental exposure data, and the more systematic use of geo-coding now provide ways to systematically assess community-level indicators of health, and link these factors to electronic health records for evaluating their influence on disease outcomes...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218919/the-diversity-and-disparity-in-biomedical-informatics-ddbi-workshop
#4
William M Southerland, S Joshua Swamidass, Philip R O Payne, Laura Wiley, ClarLynda Williams-DeVane
The Diversity and Disparity in Biomedical Informatics (DDBI) workshop will be focused on complementary and critical issues concerned with enhancing diversity in the informatics workforce as well as diversity in patient cohorts. According to the National Institute of Minority Health and Health Disparities (NIMHD) at the NIH, diversity refers to the inclusion of the following traditionally underrepresented groups: African Americans/Blacks, Asians (>30 countries), American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Latino or Hispanic (20 countries)...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218918/annotating-gene-sets-by-mining-large-literature-collections-with-protein-networks
#5
Sheng Wang, Jianzhu Ma, Michael Ku Yu, Fan Zheng, Edward W Huang, Jiawei Han, Jian Peng, Trey Ideker
Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218917/genedive-a-gene-interaction-search-and-visualization-tool-to-facilitate-precision-medicine
#6
Paul Previde, Brook Thomas, Mike Wong, Emily K Mallory, Dragutin Petkovic, Russ B Altman, Anagha Kulkarni
Obtaining relevant information about gene interactions is critical for understanding disease processes and treatment. With the rise in text mining approaches, the volume of such biomedical data is rapidly increasing, thereby creating a new problem for the users of this data: information overload. A tool for efficient querying and visualization of biomedical data that helps researchers understand the underlying biological mechanisms for diseases and drug responses, and ultimately helps patients, is sorely needed...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218916/visage-integrating-external-knowledge-into-electronic-medical-record-visualization
#7
Edward W Huang, Sheng Wang, ChengXiang Zhai
In this paper, we present VisAGE, a method that visualizes electronic medical records (EMRs) in a low-dimensional space. Effective visualization of new patients allows doctors to view similar, previously treated patients and to identify the new patients' disease subtypes, reducing the chance of misdiagnosis. However, EMRs are typically incomplete or fragmented, resulting in patients who are missing many available features being placed near unrelated patients in the visualized space. VisAGE integrates several external data sources to enrich EMR databases to solve this issue...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218915/improving-precision-in-concept-normalization
#8
Mayla Boguslav, K Bretonnel Cohen, William A Baumgartner, Lawrence E Hunter
Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distribution of false positive results, we describe a strategy for increasing precision, using a variety of both pre-processing and post-processing methods. They draw on both knowledge-based and frequentist approaches to modeling language. Based on an existing high-performance biomedical concept recognition pipeline and a previously published manually annotated corpus, we apply this hybrid rationalist/empiricist strategy to concept normalization for eight different ontologies...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218914/advances-in-text-mining-and-visualization-for-precision-medicine
#9
Graciela Gonzalez-Hernandez, Abeed Sarker, Karen O'Connor, Casey Greene, Hongfang Liu
According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Although the text mining community has explored this realm for some years, the official endorsement and funding launched in 2015 with the Precision Medicine Initiative are beginning to bear fruit. This session sought to elicit participation of researchers with strong background in text mining and/or visualization who are actively collaborating with bench scientists and clinicians for the deployment of integrative approaches in precision medicine that could impact scientific discovery and advance the vision of precision medicine as a universal, accessible approach at the point of care...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218913/leveraging-putative-enhancer-promoter-interactions-to-investigate-two-way-epistasis-in-type-2-diabetes-gwas
#10
Elisabetta Manduchi, Alessandra Chesi, Molly A Hall, Struan F A Grant, Jason H Moore
We utilized evidence for enhancer-promoter interactions from functional genomics data in order to build biological filters to narrow down the search space for two-way Single Nucleotide Polymorphism (SNP) interactions in Type 2 Diabetes (T2D) Genome Wide Association Studies (GWAS). This has led us to the identification of a reproducible statistically significant SNP pair associated with T2D. As more functional genomics data are being generated that can help identify potentially interacting enhancer-promoter pairs in larger collection of tissues/cells, this approach has implications for investigation of epistasis from GWAS in general...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218912/network-analysis-of-pseudogene-gene-relationships-from-pseudogene-evolution-to-their-functional-potentials
#11
Travis S Johnson, Sihong Li, Jonathan R Kho, Kun Huang, Yan Zhang
Pseudogenes are fossil relatives of genes. Pseudogenes have long been thought of as "junk DNAs", since they do not code proteins in normal tissues. Although most of the human pseudogenes do not have noticeable functions, ∼20% of them exhibit transcriptional activity. There has been evidence showing that some pseudogenes adopted functions as lncRNAs and work as regulators of gene expression. Furthermore, pseudogenes can even be "reactivated" in some conditions, such as cancer initiation. Some pseudogenes are transcribed in specific cancer types, and some are even translated into proteins as observed in several cancer cell lines...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218911/convergent-downstream-candidate-mechanisms-of-independent-intergenic-polymorphisms-between-co-classified-diseases-implicate-epistasis-among-noncoding-elements
#12
Jiali Han, Jianrong Li, Ikbel Achour, Lorenzo Pesce, Ian Foster, Haiquan Li, Yves A Lussier
Eighty percent of DNA outside protein coding regions was shown biochemically functional by the ENCODE project, enabling studies of their interactions. Studies have since explored how convergent downstream mechanisms arise from independent genetic risks of one complex disease. However, the cross-talk and epistasis between intergenic risks associated with distinct complex diseases have not been comprehensively characterized. Our recent integrative genomic analysis unveiled downstream biological effectors of disease-specific polymorphisms buried in intergenic regions, and we then validated their genetic synergy and antagonism in distinct GWAS...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218910/pan-cancer-analysis-of-expressed-somatic-nucleotide-variants-in-long-intergenic-non-coding-rna
#13
Travers Ching, Lana X Garmire
Long intergenic non-coding RNAs have been shown to play important roles in cancer. However, because lincRNAs are a relatively new class of RNAs compared to protein-coding mRNAs, the mutational landscape of lincRNAs has not been as extensively studied. Here we characterize expressed somatic nucleotide variants within lincRNAs using 12 cancer RNA-Seq datasets in TCGA. We build machine-learning models to discriminate somatic variants from germline variants within lincRNA regions (AUC 0.987). We build another model to differentiate lincRNA somatic mutations from background regions (AUC 0...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218909/reading-between-the-genes-computational-models-to-discover-function-from-noncoding-dna
#14
Yves A Lussier, Joanne Berghout, Francesca Vitali, Kenneth S Ramos, Maricel Kann, Jason H Moore
Noncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218908/analyzing-metabolomics-data-for-association-with-genotypes-using-two-component-gaussian-mixture-distributions
#15
Jason Westra, Nicholas Hartman, Bethany Lake, Gregory Shearer, Nathan Tintle
Standard approaches to evaluate the impact of single nucleotide polymorphisms (SNP) on quantitative phenotypes use linear models. However, these normal-based approaches may not optimally model phenotypes which are better represented by Gaussian mixture distributions (e.g., some metabolomics data). We develop a likelihood ratio test on the mixing proportions of two-component Gaussian mixture distributions and consider more restrictive models to increase power in light of a priori biological knowledge. Data were simulated to validate the improved power of the likelihood ratio test and the restricted likelihood ratio test over a linear model and a log transformed linear model...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218907/emergence-of-pathway-level-composite-biomarkers-from-converging-gene-set-signals-of-heterogeneous-transcriptomic-responses
#16
Samir Rachid Zaim, Qike Li, A Grant Schissler, Yves A Lussier
Recent precision medicine initiatives have led to the expectation of improved clinical decisionmaking anchored in genomic data science. However, over the last decade, only a handful of new single-gene product biomarkers have been translated to clinical practice (FDA approved) in spite of considerable discovery efforts deployed and a plethora of transcriptomes available in the Gene Expression Omnibus. With this modest outcome of current approaches in mind, we developed a pilot simulation study to demonstrate the untapped benefits of developing disease detection methods for cases where the true signal lies at the pathway level, even if the pathway's gene expression alterations may be heterogeneous across patients...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218906/addressing-vital-sign-alarm-fatigue-using-personalized-alarm-thresholds
#17
Sarah Poole, Nigam Shah
Alarm fatigue, a condition in which clinical staff become desensitized to alarms due to the high frequency of unnecessary alarms, is a major patient safety concern. Alarm fatigue is particularly prevalent in the pediatric setting, due to the high level of variation in vital signs with patient age. Existing studies have shown that the current default pediatric vital sign alarm thresholds are inappropriate, and lead to a larger than necessary alarm load. This study leverages a large database containing over 190 patient-years of heart rate data to accurately identify the 1st and 99th percentiles of an individual's heart rate on their first day of vital sign monitoring...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218905/considerations-for-automated-machine-learning-in-clinical-metabolic-profiling-altered-homocysteine-plasma-concentration-associated-with-metformin-exposure
#18
Alena Orlenko, Jason H Moore, Patryk Orzechowski, Randal S Olson, Junmei Cairns, Pedro J Caraballo, Richard M Weinshilboum, Liewei Wang, Matthew K Breitenstein
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218904/evaluation-of-predixcan-for-prioritizing-gwas-associations-and-predicting-gene-expression
#19
Binglan Li, Shefali S Verma, Yogasudha C Veturi, Anurag Verma, Yuki Bradford, David W Haas, Marylyn D Ritchie
Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218903/coalitional-game-theory-as-a-promising-approach-to-identify-candidate-autism-genes
#20
Anika Gupta, Min Woo Sun, Kelley Marie Paskov, Nate Tyler Stockham, Jae-Yoon Jung, Dennis Paul Wall
Despite mounting evidence for the strong role of genetics in the phenotypic manifestation of Autism Spectrum Disorder (ASD), the specific genes responsible for the variable forms of ASD remain undefined. ASD may be best explained by a combinatorial genetic model with varying epistatic interactions across many small effect mutations. Coalitional or cooperative game theory is a technique that studies the combined effects of groups of players, known as coalitions, seeking to identify players who tend to improve the performance--the relationship to a specific disease phenotype--of any coalition they join...
2018: Pacific Symposium on Biocomputing
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