keyword
MENU ▼
Read by QxMD icon Read
search

Jason H Moore

keyword
https://www.readbyqxmd.com/read/29218913/leveraging-putative-enhancer-promoter-interactions-to-investigate-two-way-epistasis-in-type-2-diabetes-gwas
#1
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/29218909/reading-between-the-genes-computational-models-to-discover-function-from-noncoding-dna
#2
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/29218905/considerations-for-automated-machine-learning-in-clinical-metabolic-profiling-altered-homocysteine-plasma-concentration-associated-with-metformin-exposure
#3
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/29218887/a-heuristic-method-for-simulating-open-data-of-arbitrary-complexity-that-can-be-used-to-compare-and-evaluate-machine-learning-methods
#4
Jason H Moore, Maksim Shestov, Peter Schmitt, Randal S Olson
A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218881/data-driven-advice-for-applying-machine-learning-to-bioinformatics-problems
#5
Randal S Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H Moore
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218875/mapping-patient-trajectories-using-longitudinal-extraction-and-deep-learning-in-the-mimic-iii-critical-care-database
#6
Brett K Beaulieu-Jones, Patryk Orzechowski, Jason H Moore
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient's interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29215023/a-pilot-characterization-of-the-human-chronobiome
#7
Carsten Skarke, Nicholas F Lahens, Seth D Rhoades, Amy Campbell, Kyle Bittinger, Aubrey Bailey, Christian Hoffmann, Randal S Olson, Lihong Chen, Guangrui Yang, Thomas S Price, Jason H Moore, Frederic D Bushman, Casey S Greene, Gregory R Grant, Aalim M Weljie, Garret A FitzGerald
Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data from remote sensors, physiological and multi-omics analyses to assess the feasibility of detecting time dependent signals - the chronobiome - despite the "noise" attributable to the behavioral differences of free-living human volunteers...
December 7, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29213332/artificial-intelligence-more-human-with-human
#8
Moshe Sipper, Jason H Moore
No abstract text is available yet for this article.
2017: BioData Mining
https://www.readbyqxmd.com/read/29208777/short-a%C3%AE-peptides-attenuate-a%C3%AE-42-toxicity-in-vivo
#9
Brenda D Moore, Jason Martin, Lorena de Mena, Jonatan Sanchez, Pedro E Cruz, Carolina Ceballos-Diaz, Thomas B Ladd, Yong Ran, Yona Levites, Thomas L Kukar, Justin J Kurian, Robert McKenna, Edward H Koo, David R Borchelt, Christopher Janus, Diego Rincon-Limas, Pedro Fernandez-Funez, Todd E Golde
Processing of amyloid-β (Aβ) precursor protein (APP) by γ-secretase produces multiple species of Aβ: Aβ40, short Aβ peptides (Aβ37-39), and longer Aβ peptides (Aβ42-43). γ-Secretase modulators, a class of Alzheimer's disease therapeutics, reduce production of the pathogenic Aβ42 but increase the relative abundance of short Aβ peptides. To evaluate the pathological relevance of these peptides, we expressed Aβ36-40 and Aβ42-43 in Drosophila melanogaster to evaluate inherent toxicity and potential modulatory effects on Aβ42 toxicity...
December 5, 2017: Journal of Experimental Medicine
https://www.readbyqxmd.com/read/29206922/pie-a-prior-knowledge-guided-integrated-likelihood-estimation-method-for-bias-reduction-in-association-studies-using-electronic-health-records-data
#10
Jing Huang, Rui Duan, Rebecca A Hubbard, Yonghui Wu, Jason H Moore, Hua Xu, Yong Chen
Objectives: This study proposes a novel Prior knowledge guided Integrated likelihood Estimation (PIE) method to correct bias in estimations of associations due to misclassification of electronic health record (EHR)-derived binary phenotypes, and evaluates the performance of the proposed method by comparing it to 2 methods in common practice. Methods: We conducted simulation studies and data analysis of real EHR-derived data on diabetes from Kaiser Permanente Washington to compare the estimation bias of associations using the proposed method, the method ignoring phenotyping errors, the maximum likelihood method with misspecified sensitivity and specificity, and the maximum likelihood method with correctly specified sensitivity and specificity (gold standard)...
December 1, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29141221/chemotherapy-induced-depletion-of-oct4-positive-cancer-stem-cells-in-a-mouse-model-of-malignant-testicular-cancer
#11
Timothy M Pierpont, Amy M Lyndaker, Claire M Anderson, Qiming Jin, Elizabeth S Moore, Jamie L Roden, Alicia Braxton, Lina Bagepalli, Nandita Kataria, Hilary Zhaoxu Hu, Jason Garness, Matthew S Cook, Blanche Capel, Donald H Schlafer, Teresa Southard, Robert S Weiss
Testicular germ cell tumors (TGCTs) are among the most responsive solid cancers to conventional chemotherapy. To elucidate the underlying mechanisms, we developed a mouse TGCT model featuring germ cell-specific Kras activation and Pten inactivation. The resulting mice developed malignant, metastatic TGCTs composed of teratoma and embryonal carcinoma, the latter of which exhibited stem cell characteristics, including expression of the pluripotency factor OCT4. Consistent with epidemiological data linking human testicular cancer risk to in utero exposures, embryonic germ cells were susceptible to malignant transformation, whereas adult germ cells underwent apoptosis in response to the same oncogenic events...
November 14, 2017: Cell Reports
https://www.readbyqxmd.com/read/29110346/on-meta-and-mega-analyses-for-gene-environment-interactions
#12
Jing Huang, Yulun Liu, Steve Vitale, Trevor M Penning, Alexander S Whitehead, Ian A Blair, Anil Vachani, Margie L Clapper, Joshua E Muscat, Philip Lazarus, Paul Scheet, Jason H Moore, Yong Chen
Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a "mega" data set, which can be fit by a joint "mega-analysis...
November 7, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29079728/plato-software-provides-analytic-framework-for-investigating-complexity-beyond-genome-wide-association-studies
#13
Molly A Hall, John Wallace, Anastasia Lucas, Dokyoon Kim, Anna O Basile, Shefali S Verma, Cathy A McCarty, Murray H Brilliant, Peggy L Peissig, Terrie E Kitchner, Anurag Verma, Sarah A Pendergrass, Scott M Dudek, Jason H Moore, Marylyn D Ritchie
Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene-environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses...
October 27, 2017: Nature Communications
https://www.readbyqxmd.com/read/29053868/lagos-ne-a-multi-scaled-geospatial-and-temporal-database-of-lake-ecological-context-and-water-quality-for-thousands-of-u-s-lakes
#14
Patricia A Soranno, Linda C Bacon, Michael Beauchene, Karen E Bednar, Edward G Bissell, Claire K Boudreau, Marvin G Boyer, Mary T Bremigan, Stephen R Carpenter, Jamie W Carr, Kendra S Cheruvelil, Samuel T Christel, Matt Claucherty, Sarah M Collins, Joseph D Conroy, John A Downing, Jed Dukett, C Emi Fergus, Christopher T Filstrup, Clara Funk, Maria J Gonzalez, Linda T Green, Corinna Gries, John D Halfman, Stephen K Hamilton, Paul C Hanson, Emily N Henry, Elizabeth M Herron, Celeste Hockings, James R Jackson, Kari Jacobson-Hedin, Lorraine L Janus, William W Jones, John R Jones, Caroline M Keson, Katelyn B S King, Scott A Kishbaugh, Jean-Francois Lapierre, Barbara Lathrop, Jo A Latimore, Yuehlin Lee, Noah R Lottig, Jason A Lynch, Leslie J Matthews, William H McDowell, Karen E B Moore, Brian P Neff, Sarah J Nelson, Samantha K Oliver, Michael L Pace, Donald C Pierson, Autumn C Poisson, Amina I Pollard, David M Post, Paul O Reyes, Donald O Rosenberry, Karen M Roy, Lars G Rudstam, Orlando Sarnelle, Nancy J Schuldt, Caren E Scott, Nicholas K Skaff, Nicole J Smith, Nick R Spinelli, Joseph J Stachelek, Emily H Stanley, John L Stoddard, Scott B Stopyak, Craig A Stow, Jason M Tallant, Pang-Ning Tan, Anthony P Thorpe, Michael J Vanni, Tyler Wagner, Gretchen Watkins, Kathleen C Weathers, Katherine E Webster, Jeffrey D White, Marcy K Wilmes, Shuai Yuan
Background: Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many datasets across broad regions and across time into an integrated database...
October 19, 2017: GigaScience
https://www.readbyqxmd.com/read/29044470/analysis-of-gene-gene-interactions
#15
Brian S Cole, Molly A Hall, Ryan J Urbanowicz, Diane Gilbert-Diamond, Jason H Moore
The goal of this unit is to introduce epistasis, or gene-gene interactions, as a significant contributor to the genetic architecture of complex traits, including disease susceptibility. This unit begins with an historical overview of the concept of epistasis and the challenges inherent in the identification of potential gene-gene interactions. Then, it reviews statistical and machine learning methods for discovering epistasis in the context of genetic studies of quantitative and categorical traits. This unit concludes with a discussion of meta-analysis, replication, and other topics of active research...
October 18, 2017: Current Protocols in Human Genetics
https://www.readbyqxmd.com/read/29023970/phenotype-validation-in-electronic-health-records-based-genetic-association-studies
#16
Lu Wang, Scott M Damrauer, Hong Zhang, Alan X Zhang, Rui Xiao, Jason H Moore, Jinbo Chen
The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case-control sampling strategy to select subjects for phenotype validation...
October 11, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28978191/incorporation-of-biological-knowledge-into-the-study-of-gene-environment-interactions
#17
Marylyn D Ritchie, Joe R Davis, Hugues Aschard, Alexis Battle, David Conti, Mengmeng Du, Eleazar Eskin, M Daniele Fallin, Li Hsu, Peter Kraft, Jason H Moore, Brandon L Pierce, Stephanie A Bien, Duncan C Thomas, Peng Wei, Stephen B Montgomery
A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach...
October 1, 2017: American Journal of Epidemiology
https://www.readbyqxmd.com/read/28957327/up-for-a-challenge-u4c-stimulating-innovation-in-breast-cancer-genetic-epidemiology
#18
EDITORIAL
Leah E Mechanic, Sara Lindström, Kenneth M Daily, Solveig K Sieberts, Christopher I Amos, Huann-Sheng Chen, Nancy J Cox, Marina Dathe, Eric J Feuer, Michael J Guertin, Joshua Hoffman, Yunxian Liu, Jason H Moore, Chad L Myers, Marylyn D Ritchie, Joellen Schildkraut, Fredrick Schumacher, John S Witte, Wen Wang, Scott M Williams, Elizabeth M Gillanders
No abstract text is available yet for this article.
September 2017: PLoS Genetics
https://www.readbyqxmd.com/read/28944497/evolutionarily-derived-networks-to-inform-disease-pathways
#19
Britney E Graham, Christian Darabos, Minjun Huang, Louis J Muglia, Jason H Moore, Scott M Williams
Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET)...
December 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28770004/discovery-and-replication-of-snp-snp-interactions-for-quantitative-lipid-traits-in-over-60-000-individuals
#20
Emily R Holzinger, Shefali S Verma, Carrie B Moore, Molly Hall, Rishika De, Diane Gilbert-Diamond, Matthew B Lanktree, Nathan Pankratz, Antoinette Amuzu, Amber Burt, Caroline Dale, Scott Dudek, Clement E Furlong, Tom R Gaunt, Daniel Seung Kim, Helene Riess, Suthesh Sivapalaratnam, Vinicius Tragante, Erik P A van Iperen, Ariel Brautbar, David S Carrell, David R Crosslin, Gail P Jarvik, Helena Kuivaniemi, Iftikhar J Kullo, Eric B Larson, Laura J Rasmussen-Torvik, Gerard Tromp, Jens Baumert, Karen J Cruickshanks, Martin Farrall, Aroon D Hingorani, G K Hovingh, Marcus E Kleber, Barbara E Klein, Ronald Klein, Wolfgang Koenig, Leslie A Lange, Winfried Mӓrz, Kari E North, N Charlotte Onland-Moret, Alex P Reiner, Philippa J Talmud, Yvonne T van der Schouw, James G Wilson, Mika Kivimaki, Meena Kumari, Jason H Moore, Fotios Drenos, Folkert W Asselbergs, Brendan J Keating, Marylyn D Ritchie
BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). RESULTS: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples...
2017: BioData Mining
keyword
keyword
114269
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"