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Pacific Symposium on Biocomputing

Abeed Sarker, Azadeh Nikfarjam, Graciela Gonzalez
Social media has evolved into a crucial resource for obtaining large volumes of real-time information. The promise of social media has been realized by the public health domain, and recent research has addressed some important challenges in that domain by utilizing social media data. Tasks such as monitoring flu trends, viral disease outbreaks, medication abuse, and adverse drug reactions are some examples of studies where data from social media have been exploited. The focus of this workshop is to explore solutions to three important natural language processing challenges for domain-specific social media text: (i) text classification, (ii) information extraction, and (iii) concept normalization...
2016: Pacific Symposium on Biocomputing
Eric K Neumann, Svetlana Lockwood, Bala Krishnamoorthy, David Spivak
No abstract text is available yet for this article.
2016: Pacific Symposium on Biocomputing
Steven E Brenner, Stephen Kingsmore, Sean D Mooney, Robert Nussbaum, Jennifer Puck
Rare genetic disorders affect millions of individuals worldwide. Many of these disorders can take decades to correctly diagnose. Because of this, genome sequencing of newborns raises a substantial opportunity to identify genetic disorders before they present symptoms, and to identify patient risks at the start of life. Many of these disorders can take decades to correctly diagnose. Because of this, genome sequencing of newborns raises a substantial opportunity to identify genetic disorders before they present symptoms, and to identify patient risks at the start of life...
2016: Pacific Symposium on Biocomputing
Casey S Greene, James A Foster, Bruce A Stanton, Deborah A Hogan, Yana Bromberg
Technological advances are making large-scale measurements of microbial communities commonplace. These newly acquired datasets are allowing researchers to ask and answer questions about the composition of microbial communities, the roles of members in these communities, and how genes and molecular pathways are regulated in individual community members and communities as a whole to effectively respond to diverse and changing environments. In addition to providing a more comprehensive survey of the microbial world, this new information allows for the development of computational approaches to model the processes underlying microbial systems...
2016: Pacific Symposium on Biocomputing
Ingmar Weber, Palakorn Achananuparp
To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction...
2016: Pacific Symposium on Biocomputing
Ryan Sullivan, Abeed Sarker, Karen O'Connor, Amanda Goodin, Mark Karlsrud, Graciela Gonzalez
Although dietary supplements are widely used and generally are considered safe, some supplements have been identified as causative agents for adverse reactions, some of which may even be fatal. The Food and Drug Administration (FDA) is responsible for monitoring supplements and ensuring that supplements are safe. However, current surveillance protocols are not always effective. Leveraging user-generated textual data, in the form of reviews for nutritional supplements, we use natural language processing techniques to develop a system for the monitoring of dietary supplements...
2016: Pacific Symposium on Biocomputing
H Andrew Schwartz, Maarten Sap, Margaret L Kern, Johannes C Eichstaedt, Adam Kapelner, Megha Agrawal, Eduardo Blanco, Lukasz Dziurzynski, Gregory Park, David Stillwell, Michal Kosinski, Martin E P Seligman, Lyle H Ungar
We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being...
2016: Pacific Symposium on Biocomputing
Bahadorreza Ofoghi, Meghan Mann, Karin Verspoor
Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. In this paper, we explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early discovery or alarming of such events. We analyse user tweets around recent incidents of Ebola, finding differences in the expression of emotions in tweets posted prior to and after the incidents have emerged...
2016: Pacific Symposium on Biocomputing
Rion Brattig Correia, Lang Li, Luis M Rocha
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health...
2016: Pacific Symposium on Biocomputing
Yin Aphinyanaphongs, Armine Lulejian, Duncan Penfold Brown, Richard Bonneau, Paul Krebs
Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system...
2016: Pacific Symposium on Biocomputing
Lourdes Peña-Castillo, Marc Grüell, Martin E Mulligan, Andrew S Lang
Small non-coding RNAs (sRNAs) are regulatory RNA molecules that have been identified in a multitude of bacterial species and shown to control numerous cellular processes through various regulatory mechanisms. In the last decade, next generation RNA sequencing (RNA-seq) has been used for the genome-wide detection of bacterial sRNAs. Here we describe sRNA-Detect, a novel approach to identify expressed small transcripts from prokaryotic RNA-seq data. Using RNA-seq data from three bacterial species and two sequencing platforms, we performed a comparative assessment of five computational approaches for the detection of small transcripts...
2016: Pacific Symposium on Biocomputing
Usha Muppirala, Benjamin A Lewis, Carla M Mann, Drena Dobbs
Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for "partner-specific" interfacial residue prediction...
2016: Pacific Symposium on Biocomputing
Wanja Kassuhn, Uwe Ohler, Philipp Drewe
CLIP-Seq protocols such as PAR-CLIP, HITS-CLIP or iCLIP allow a genome-wide analysis of protein-RNA interactions. For the processing of the resulting short read data, various tools are utilized. Some of these tools were specifically developed for CLIP-Seq data, whereas others were designed for the analysis of RNA-Seq data. To this date, however, it has not been assessed which of the available tools are most appropriate for the analysis of CLIP-Seq data. This is because an experimental gold standard dataset on which methods can be accessed and compared, is still not available...
2016: Pacific Symposium on Biocomputing
Nattapon Thanintorn, Juexin Wang, Ilker Ersoy, Zainab Al-Taie, Yuexu Jiang, Duolin Wang, Megha Verma, Trupti Joshi, Richard Hammer, Dong Xu, Dmitriy Shin
Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets...
2016: Pacific Symposium on Biocomputing
Artem Sokolov, Evan O Paull, Joshua M Stuart
The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy...
2016: Pacific Symposium on Biocomputing
Subhajit Sengupta, Tianjian Zhou, Peter Müeller, Yuan Ji
We present a feature allocation model to reconstruct tumor subclones based on mutation pairs. The key innovation lies in the use of a pair of proximal single nucleotide variants (SNVs) for the subclone reconstruction as opposed to a single SNV. Using the categorical extension of the Indian buffet process (cIBP) we define the subclones as a vector of categorical matrices corresponding to a set of mutation pairs. Through Bayesian inference we report posterior probabilities of the number, genotypes and population frequencies of subclones in one or more tumor sample...
2016: Pacific Symposium on Biocomputing
Yong Fuga Li, Fuxiao Xin, Russ B Altman
The causes of complex diseases are multifactorial and the phenotypes of complex diseases are typically heterogeneous, posting significant challenges for both the experiment design and statistical inference in the study of such diseases. Transcriptome profiling can potentially provide key insights on the pathogenesis of diseases, but the signals from the disease causes and consequences are intertwined, leaving it to speculations what are likely causal. Genome-wide association study on the other hand provides direct evidences on the potential genetic causes of diseases, but it does not provide a comprehensive view of disease pathogenesis, and it has difficulties in detecting the weak signals from individual genes...
2016: Pacific Symposium on Biocomputing
Sarah M Laper, Nicole A Restrepo, Dana C Crawford
Access and utilization of electronic health records with extensive medication lists and genetic profiles is rapidly advancing discoveries in pharmacogenomics. In this study, we analyzed ~116,000 variants on the Illumina Metabochip for response to antihypertensive and lipid lowering medications in African American adults from BioVU, the Vanderbilt University Medical Center's biorepository linked to de-identified electronic health records. Our study population included individuals who were prescribed an antihypertensive or lipid lowering medication, and who had both pre- and post-medication blood pressure or low-density lipoprotein cholesterol (LDL-C) measurements, respectively...
2016: Pacific Symposium on Biocomputing
Dokyoon Kim, Anastasia Lucas, Joseph Glessner, Shefali S Verma, Yuki Bradford, Ruowang Li, Alex T Frase, Hakon Hakonarson, Peggy Peissig, Murray Brilliant, Marylyn D Ritchie
Recent studies on copy number variation (CNV) have suggested that an increasing burden of CNVs is associated with susceptibility or resistance to disease. A large number of genes or genomic loci contribute to complex diseases such as autism. Thus, total genomic copy number burden, as an accumulation of copy number change, is a meaningful measure of genomic instability to identify the association between global genetic effects and phenotypes of interest. However, no systematic annotation pipeline has been developed to interpret biological meaning based on the accumulation of copy number change across the genome associated with a phenotype of interest...
2016: Pacific Symposium on Biocomputing
Sahand Khakabimamaghani, Martin Ester
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification...
2016: Pacific Symposium on Biocomputing
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