Extending Classification Algorithms to Case-Control Studies

Stanfill, Bryan A.; Reehl, Sarah M.; Bramer, Lisa M.; Nakayasu, Ernesto S.; Rich, Stephen S.; Metz, Thomas O.; Rewers, Marian; Webb-Robertson, Bobbie-Jo M.

doi:10.1177/1179597219858954

Title: Extending Classification Algorithms to Case-Control Studies

Abstract

Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.

Authors:

^[1]; Reehl, Sarah M. ^[1];

^[1];

^[1]; Rich, Stephen S. ^[2];

^[1]; Rewers, Marian ^[3]; Webb-Robertson, Bobbie-Jo M. ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Univ. of Virginia, Charlottesville, VA (United States)
Univ. of Colorado Denver, Aurora, CO (United States)

Publication Date:: 2019-07-15

Research Org.:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

Contributing Org.:: TEDDY Study Group

OSTI Identifier:: 1556893

Report Number(s):: PNNL-SA-135302
Journal ID: ISSN 1179-5972

Grant/Contract Number:: AC05-76RL01830

Resource Type:: Accepted Manuscript

Journal Name:: Biomedical Engineering and Computational Biology

Additional Journal Information:: Journal Volume: 10; Journal ID: ISSN 1179-5972

Publisher:: SAGE

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Diabetes; machine learning; support vector machines; biomarker discovery; variable selection

Citation Formats


                    Stanfill, Bryan A., Reehl, Sarah M., Bramer, Lisa M., Nakayasu, Ernesto S., Rich, Stephen S., Metz, Thomas O., Rewers, Marian, and Webb-Robertson, Bobbie-Jo M.. Extending Classification Algorithms to Case-Control Studies.  United States: N. p., 2019. 
Web.  doi:10.1177/1179597219858954.

Copy to clipboard


                    Stanfill, Bryan A., Reehl, Sarah M., Bramer, Lisa M., Nakayasu, Ernesto S., Rich, Stephen S., Metz, Thomas O., Rewers, Marian, & Webb-Robertson, Bobbie-Jo M.. Extending Classification Algorithms to Case-Control Studies.  United States.  https://doi.org/10.1177/1179597219858954

Copy to clipboard


                    Stanfill, Bryan A., Reehl, Sarah M., Bramer, Lisa M., Nakayasu, Ernesto S., Rich, Stephen S., Metz, Thomas O., Rewers, Marian, and Webb-Robertson, Bobbie-Jo M.. Mon .  
"Extending Classification Algorithms to Case-Control Studies".  United States.  https://doi.org/10.1177/1179597219858954.  https://www.osti.gov/servlets/purl/1556893.

Copy to clipboard


                    
@article{osti_1556893,

  title        = {Extending Classification Algorithms to Case-Control Studies},

  author       = {Stanfill, Bryan A. and Reehl, Sarah M. and Bramer, Lisa M. and Nakayasu, Ernesto S. and Rich, Stephen S. and Metz, Thomas O. and Rewers, Marian and Webb-Robertson, Bobbie-Jo M.},

  abstractNote = {Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.},

  doi          = {10.1177/1179597219858954},

  journal      = {Biomedical Engineering and Computational Biology},

  number       = ,

  volume       = 10,

  place        = {United States},

  year         = {2019},

  month        = {7}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1177/1179597219858954

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Activation of natural killer T cells by α-galactosylceramide treatment prevents the onset and recurrence of autoimmune Type 1 diabetes
journal, September 2001

Sharif, Shayan; Arreaza, Guillermo A.; Zucker, Peter
Nature Medicine, Vol. 7, Issue 9
DOI: 10.1038/nm0901-1057

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes
journal, June 2007

Todd, John A.; Walker, Neil M.; Cooper, Jason D.
Nature Genetics, Vol. 39, Issue 7
DOI: 10.1038/ng2068

Defective methionine metabolism in the brain after repeated blast exposures might contribute to increased oxidative stress
journal, January 2018

Arun, Peethambaran; Rittase, William B.; Wilder, Donna M.
Neurochemistry International, Vol. 112
DOI: 10.1016/j.neuint.2017.07.014

Autoantibody Response to CD38 in Caucasian Patients With Type 1 and Type 2 Diabetes: Immunological and Genetic Characterization
journal, April 2001

Mallone, R.; Ortolan, E.; Baj, G.
Diabetes, Vol. 50, Issue 4
DOI: 10.2337/diabetes.50.4.752

Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation
journal, January 2009

Rose, Sherri; van der Laan, Mark J.
The International Journal of Biostatistics, Vol. 5, Issue 1
DOI: 10.2202/1557-4679.1127

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package
journal, January 2014

Reid, Stephen; Tibshirani, Rob
Journal of Statistical Software, Vol. 58, Issue 12
DOI: 10.18637/jss.v058.i12

Identification of a panel of sensitive and specific DNA methylation markers for lung adenocarcinoma
journal, January 2007

Tsou, Jeffrey A.; Galler, Janice S.; Siegmund, Kimberly D.
Molecular Cancer, Vol. 6, Issue 1
DOI: 10.1186/1476-4598-6-70

TEDDY-The Environmental Determinants of Diabetes in the Young: An Observational Clinical Trial
journal, October 2006

Hagopian, W. A.; Lernmark, A.; Rewers, M. J.
Annals of the New York Academy of Sciences, Vol. 1079, Issue 1
DOI: 10.1196/annals.1375.049

MissForest--non-parametric missing value imputation for mixed-type data
journal, October 2011

Stekhoven, D. J.; Buhlmann, P.
Bioinformatics, Vol. 28, Issue 1
DOI: 10.1093/bioinformatics/btr597

Variable importance in matched case-control studies in settings of high dimensional data
journal, March 2014

Balasubramanian, Raji; Andres Houseman, E.; Coull, Brent A.
Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 63, Issue 4
DOI: 10.1111/rssc.12056

Use of dicarboxylic acids in type 2 diabetes: Dicarboxylic acids for type 2 diabetes
journal, February 2013

Mingrone, Geltrude; Castagneto-Gissey, Lidia; Macé, Katherine
British Journal of Clinical Pharmacology, Vol. 75, Issue 3
DOI: 10.1111/j.1365-2125.2012.04177.x

Synthesis and characterisation of galactosyl glycerol by β-galactosidase catalysed reverse hydrolysis of galactose and glycerol
journal, December 2013

Wei, Wei; Qi, Danping; Zhao, Hai-zhen
Food Chemistry, Vol. 141, Issue 3
DOI: 10.1016/j.foodchem.2013.05.145

Downregulation of Long Noncoding RNA Meg3 Affects Insulin Synthesis and Secretion in Mouse Pancreatic Beta Cells: DOWNREGULATION OF LONG NONCODING RNA Meg3
journal, September 2015

You, LiangHui; Wang, Ning; Yin, DanDan
Journal of Cellular Physiology, Vol. 231, Issue 4
DOI: 10.1002/jcp.25175

Improving statistical analysis of matched case-control studies
journal, February 2013

Conway, Aaron; Rolley, John X.; Fulbrook, Paul
Research in Nursing & Health, Vol. 36, Issue 3
DOI: 10.1002/nur.21536

Leucine metabolism in regulation of insulin secretion from pancreatic beta cells: Nutrition Reviews©, Vol. 68, No. 5
journal, April 2010

Yang, Jichun; Chi, Yujing; Burkhardt, Brant R.
Nutrition Reviews, Vol. 68, Issue 5
DOI: 10.1111/j.1753-4887.2010.00282.x

Second-generation PLINK: rising to the challenge of larger and richer datasets
journal, February 2015

Chang, Christopher C.; Chow, Carson C.; Tellier, Laurent CAM
GigaScience, Vol. 4, Issue 1
DOI: 10.1186/s13742-015-0047-8

Low vitamin E status is a potential risk factor for insulin-dependent diabetes mellitus
journal, January 1999

Knekt, P.; Reunanen, A.; Marniemi, J.
Journal of Internal Medicine, Vol. 245, Issue 1
DOI: 10.1046/j.1365-2796.1999.00416.x

Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients: Variable Selection and Prediction Using a Nested, Matched Case-Control Study
journal, December 2013

Qian, Jing; Payabvash, Seyedmehdi; Kemmling, André
Biometrics, Vol. 70, Issue 1
DOI: 10.1111/biom.12113

Human enterovirus infections in children at increased risk for type 1 diabetes: the Babydiet study
journal, September 2011

Simonen-Tikka, M. -L.; Pflueger, M.; Klemola, P.
Diabetologia, Vol. 54, Issue 12
DOI: 10.1007/s00125-011-2305-3

Estimation of Multiple Relative risk Functions in Matched Case-Control Studies
journal, October 1978

Breslow, N. E.; Day, N. E.; Halvorsen, K. T.
American Journal of Epidemiology, Vol. 108, Issue 4
DOI: 10.1093/oxfordjournals.aje.a112623

Omega-3 Polyunsaturated Fatty Acid Intake and Islet Autoimmunity in Children at Increased Risk for Type 1 Diabetes
journal, September 2007

Norris, Jill M.; Yin, Xiang; Lamb, Molly M.
JAMA, Vol. 298, Issue 12
DOI: 10.1001/jama.298.12.1420

Fatty acid status in infancy is associated with the risk of type 1 diabetes-associated autoimmunity
journal, May 2017

Niinistö, Sari; Takkinen, Hanna-Mari; Erlund, Iris
Diabetologia, Vol. 60, Issue 7
DOI: 10.1007/s00125-017-4280-9

High-dose vitamin E supplementation normalizes retinal blood flow and creatinine clearance in patients with type 1 diabetes
journal, August 1999

Bursell, S. E.; Clermont, A. C.; Aiello, L. P.
Diabetes Care, Vol. 22, Issue 8
DOI: 10.2337/diacare.22.8.1245

kernlab - An S4 Package for Kernel Methods in R
journal, January 2004

Karatzoglou, Alexandros; Smola, Alex; Hornik, Kurt
Journal of Statistical Software, Vol. 11, Issue 9
DOI: 10.18637/jss.v011.i09

Osmotic stress in Synechocystis sp. PCC 6803: low tolerance towards nonionic osmotic stress results from lacking activation of glucosylglycerol accumulation
journal, July 2006

Marin, K.
Microbiology, Vol. 152, Issue 7
DOI: 10.1099/mic.0.28771-0

Serum α- and γ-tocopherol concentrations and risk of advanced beta cell autoimmunity in children with HLA-conferred susceptibility to type 1 diabetes mellitus
journal, March 2008

Uusitalo, L.; Nevalainen, J.; Niinistö, S.
Diabetologia, Vol. 51, Issue 5
DOI: 10.1007/s00125-008-0959-2

Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm
journal, April 2015

Avalos, Marta; Pouyes, Hélène; Grandvalet, Yves
BMC Bioinformatics, Vol. 16, Issue S6
DOI: 10.1186/1471-2105-16-S6-S1

Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study: Biomarker Discovery Study Design
journal, July 2014

Lee, Hye-Seung; Burkhardt, Brant R.; McLeod, Wendy
Diabetes/Metabolism Research and Reviews, Vol. 30, Issue 5
DOI: 10.1002/dmrr.2510

Bayesian Variable Selection Methods for Matched Case-Control Studies
journal, January 2017

Asafu-Adjei, Josephine; Tadesse, Mahlet G.; Coull, Brent
The International Journal of Biostatistics, Vol. 13, Issue 1
DOI: 10.1515/ijb-2016-0043

ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
journal, January 2017

Wright, Marvin N.; Ziegler, Andreas
Journal of Statistical Software, Vol. 77, Issue 1
DOI: 10.18637/jss.v077.i01

Effect of Oral Sebacic Acid on Postprandial Glycemia, Insulinemia, and Glucose Rate of Appearance in Type 2 Diabetes
journal, August 2010

Iaconelli, A.; Gastaldelli, A.; Chiellini, C.
Diabetes Care, Vol. 33, Issue 11
DOI: 10.2337/dc10-0663

New potential biomarkers in the diagnosis of esophageal squamous cell carcinoma
journal, April 2009

Xu, Shu-Yong; Liu, Zan; Ma, Wen-Jing
Biomarkers, Vol. 14, Issue 5
DOI: 10.1080/13547500902903055

Similarities in Serum Acylcarnitine Patterns in Type 1 and Type 2 Diabetes Mellitus and in Metabolic Syndrome
journal, January 2013

Bene, Judit; Márton, Magdolna; Mohás, Márton
Annals of Nutrition and Metabolism, Vol. 62, Issue 1
DOI: 10.1159/000345759

α-Hydroxybutyric Acid Is a Selective Metabolite Biomarker of Impaired Glucose Tolerance
journal, April 2016

Cobb, Jeff; Eckhart, Andrea; Motsinger-Reif, Alison
Diabetes Care, Vol. 39, Issue 6
DOI: 10.2337/dc15-2752

α-Hydroxybutyrate Is an Early Biomarker of Insulin Resistance and Glucose Intolerance in a Nondiabetic Population
journal, May 2010

Gall, Walter E.; Beebe, Kirk; Lawton, Kay A.
PLoS ONE, Vol. 5, Issue 5
DOI: 10.1371/journal.pone.0010883

Support-vector networks
journal, September 1995

Cortes, Corinna; Vapnik, Vladimir
Machine Learning, Vol. 20, Issue 3
DOI: 10.1007/BF00994018

Bayesian analysis of pair-matched case-control studies subject to outcome misclassification: Analysis of matched case-control studies under misclassification
journal, August 2017

Högg, Tanja; Petkau, John; Zhao, Yinshan
Statistics in Medicine, Vol. 36, Issue 26
DOI: 10.1002/sim.7427

Pharmacological Inhibition of Glucosylceramide Synthase Enhances Insulin Sensitivity
journal, February 2007

Aerts, J. M.; Ottenhoff, R.; Powlson, A. S.
Diabetes, Vol. 56, Issue 5
DOI: 10.2337/db06-1619

Boosting for Correlated Binary Classification
journal, January 2010

Adewale, Adeniyi J.; Dinu, Irina; Yasui, Yutaka
Journal of Computational and Graphical Statistics, Vol. 19, Issue 1
DOI: 10.1198/jcgs.2009.07118

Brain lesion classification using 3T MRS spectra and paired SVM kernels
journal, July 2011

Dimou, Ioannis; Tsougos, Ioannis; Tsolaki, Evaggelia
Biomedical Signal Processing and Control, Vol. 6, Issue 3
DOI: 10.1016/j.bspc.2011.01.001

Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer
journal, January 2008

Anglim, Paul P.; Galler, Janice S.; Koss, Michael N.
Molecular Cancer, Vol. 7, Issue 1
DOI: 10.1186/1476-4598-7-62

Upregulation of lncRNA MEG3 promotes hepatic insulin resistance via increasing FoxO1 expression
journal, January 2016

Zhu, Xiang; Wu, Yuan-Bo; Zhou, Jian
Biochemical and Biophysical Research Communications, Vol. 469, Issue 2
DOI: 10.1016/j.bbrc.2015.11.048

Accuracy of dementia diagnosis--a direct comparison between radiologists and a computerized method
journal, June 2008

Kloppel, S.; Stonnington, C. M.; Barnes, J.
Brain, Vol. 131, Issue 11
DOI: 10.1093/brain/awn239

Serum α-Tocopherol Concentrations and Risk of Type 1 Diabetes Mellitus: A Cohort Study in Siblings of Affected Children
journal, January 2005

Uusitalo, L.; Knip, M.; Kenward, M. G.
Journal of Pediatric Endocrinology and Metabolism, Vol. 18, Issue 12
DOI: 10.1515/JPEM.2005.18.12.1409

Random Forests
journal, January 2001

Breiman, Leo
Machine Learning, Vol. 45, Issue 1, p. 5-32
DOI: 10.1023/A:1010933404324

Decreased plasma levels of select very long chain ceramide species Are associated with the development of nephropathy in type 1 diabetes
journal, October 2014

Klein, Richard L.; Hammad, Samar M.; Baker, Nathaniel L.
Metabolism, Vol. 63, Issue 10
DOI: 10.1016/j.metabol.2014.07.001

miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides
journal, September 2020

Meher, Prabina Kumar; Satpathy, Subhrajit; Rao, Atmakuri Ramakrishna
Scientific Reports, Vol. 10, Issue 1
DOI: 10.1038/s41598-020-71381-4

Bayesian Variable Selection Methods for Matched Case-Control Studies
text, January 2017

Rebecca, Betensky,; Brent, Coull,; Michael, Lev,
The University of North Carolina at Chapel Hill University Libraries
DOI: 10.17615/m7hv-g241

Second-generation PLINK: rising to the challenge of larger and richer datasets
text, January 2014

Chang, Christopher C.; Chow, Carson C.; Tellier, Laurent C. A. M.
arXiv
DOI: 10.48550/arxiv.1410.4803

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
text, January 2015

Wright, Marvin N.; Ziegler, Andreas
arXiv
DOI: 10.48550/arxiv.1508.04409

Improving statistical analysis of matched case-control studies
journal, February 2013

Conway, Aaron; Rolley, John X.; Fulbrook, Paul
Research in Nursing & Health, Vol. 36, Issue 3
DOI: 10.1002/nur.21536

Human enterovirus infections in children at increased risk for type 1 diabetes: the Babydiet study
journal, September 2011

Simonen-Tikka, M. -L.; Pflueger, M.; Klemola, P.
Diabetologia, Vol. 54, Issue 12
DOI: 10.1007/s00125-011-2305-3

Decreased plasma levels of select very long chain ceramide species Are associated with the development of nephropathy in type 1 diabetes
journal, October 2014

Klein, Richard L.; Hammad, Samar M.; Baker, Nathaniel L.
Metabolism, Vol. 63, Issue 10
DOI: 10.1016/j.metabol.2014.07.001

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes
journal, June 2007

Todd, John A.; Walker, Neil M.; Cooper, Jason D.
Nature Genetics, Vol. 39, Issue 7
DOI: 10.1038/ng2068

New potential biomarkers in the diagnosis of esophageal squamous cell carcinoma
journal, April 2009

Xu, Shu-Yong; Liu, Zan; Ma, Wen-Jing
Biomarkers, Vol. 14, Issue 5
DOI: 10.1080/13547500902903055

MissForest--non-parametric missing value imputation for mixed-type data
journal, October 2011

Stekhoven, D. J.; Buhlmann, P.
Bioinformatics, Vol. 28, Issue 1
DOI: 10.1093/bioinformatics/btr597

Accuracy of dementia diagnosis--a direct comparison between radiologists and a computerized method
journal, June 2008

Kloppel, S.; Stonnington, C. M.; Barnes, J.
Brain, Vol. 131, Issue 11
DOI: 10.1093/brain/awn239

Similarities in Serum Acylcarnitine Patterns in Type 1 and Type 2 Diabetes Mellitus and in Metabolic Syndrome
journal, January 2013

Bene, Judit; Márton, Magdolna; Mohás, Márton
Annals of Nutrition and Metabolism, Vol. 62, Issue 1
DOI: 10.1159/000345759

ω-3 polyunsaturated fatty acids ameliorate type 1 diabetes and autoimmunity
journal, April 2017

Bi, Xinyun; Li, Fanghong; Liu, Shanshan
Journal of Clinical Investigation, Vol. 127, Issue 5
DOI: 10.1172/jci87388

Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer
journal, January 2008

Anglim, Paul P.; Galler, Janice S.; Koss, Michael N.
Molecular Cancer, Vol. 7, Issue 1
DOI: 10.1186/1476-4598-7-62

Second-generation PLINK: rising to the challenge of larger and richer datasets
journal, February 2015

Chang, Christopher C.; Chow, Carson C.; Tellier, Laurent CAM
GigaScience, Vol. 4, Issue 1
DOI: 10.1186/s13742-015-0047-8

TEDDY-The Environmental Determinants of Diabetes in the Young: An Observational Clinical Trial
journal, October 2006

Hagopian, W. A.; Lernmark, A.; Rewers, M. J.
Annals of the New York Academy of Sciences, Vol. 1079, Issue 1
DOI: 10.1196/annals.1375.049

α-Hydroxybutyrate Is an Early Biomarker of Insulin Resistance and Glucose Intolerance in a Nondiabetic Population
journal, May 2010

Gall, Walter E.; Beebe, Kirk; Lawton, Kay A.
PLoS ONE, Vol. 5, Issue 5
DOI: 10.1371/journal.pone.0010883

Anti-CD38 Autoimmunity in Children with Newly Diagnosed Type 1 Diabetes Mellitus
journal, January 2005

Pupilli, C.; Antonelli, A.; Iughetti, L.
Journal of Pediatric Endocrinology and Metabolism, Vol. 18, Issue 12
DOI: 10.1515/jpem.2005.18.12.1417

Effect of Oral Sebacic Acid on Postprandial Glycemia, Insulinemia, and Glucose Rate of Appearance in Type 2 Diabetes
journal, August 2010

Iaconelli, A.; Gastaldelli, A.; Chiellini, C.
Diabetes Care, Vol. 33, Issue 11
DOI: 10.2337/dc10-0663

α-Hydroxybutyric Acid Is a Selective Metabolite Biomarker of Impaired Glucose Tolerance
journal, April 2016

Cobb, Jeff; Eckhart, Andrea; Motsinger-Reif, Alison
Diabetes Care, Vol. 39, Issue 6
DOI: 10.2337/dc15-2752

High-dose vitamin E supplementation normalizes retinal blood flow and creatinine clearance in patients with type 1 diabetes
journal, August 1999

Bursell, S. E.; Clermont, A. C.; Aiello, L. P.
Diabetes Care, Vol. 22, Issue 8
DOI: 10.2337/diacare.22.8.1245

Similar Records in DOE PAGES and OSTI.GOV collections:

Convolutional Neural Networks for Hydrometeor Classification using Dual Polarization Doppler Radars

Conference Lu, Yuping ; Kumar, Jitendra

Traditional fuzzy logic hydrometeor classification algorithm is a common way to classify precipitation type from dual polarization doppler radar. We propose a deep learning-based method to estimate hydrometeors efficiently using observed radar variables such as horizontal reflectivity (Z H ), differential reflectivity (Z DR ), correlation coefficient (ρ HV ) and specific differential phase (K DP ) from National Weather Service NEXRAD collected at Vance AFB facility at the first elevation angle from January 1st, 2015 to July 31th, 2019. We stack matrices of values from these four polarimetric variables as one 3D array. Samples are preprocessed and divided intomore »« less
https://doi.org/10.1109/ICDMW.2019.00050

Full Text Available
Convolutional Neural Networks for Hydrometeor Classification using Dual Polarization Doppler Radars

Conference Lu, Yuping ; Kumar, Jitendra

Traditional fuzzy logic hydrometeor classification algorithm is a common way to classify precipitation type from dual polarization doppler radar. A new deep learning-based method is proposed to estimate hydrometeors efficiently using observed radar variables such as horizontal reflectivity (ZH), differential reflectivity (ZDR), correlation coefficient (HV ) and specific differential phase (KDP ) from National Weather Service NEXRAD collected at Vance AFB facility at the first elevation angle from January 1st, 2015 to July 31th, 2019. We stack matrices of values from these four polarimetric variables as one 3D array. Samples are preprocessed and divided into training, validation and test setmore »« less
https://doi.org/10.1109/ICDMW.2019.00050

Full Text Available
A novel procedure on next generation sequencing data analysis using text mining algorithm

Journal Article Zhao, Weizhong ; Chen, James J. ; Perkins, Roger ; ... - BMC Bioinformatics

Background: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Methods: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and datamore »« less
https://doi.org/10.1186/s12859-016-1075-9

Full Text Available
Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

Journal Article Webb‐Robertson, Bobbie‐Jo M. ; Bramer, Lisa M. ; Stanfill, Bryan A. ; ... - Journal of Diabetes (Online)

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified amore »« less
https://doi.org/10.1111/1753-0407.13093

Full Text Available
MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Journal Article Young, M ; Craft, D - Medical Physics

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore »« less
https://doi.org/10.1118/1.4957252

Other research related to this record:

Online_Appendix_Final – Supplemental material for Extending Classification Algorithms to Case-Control Studies
text, January 2019

Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa
Figshare
DOI: 10.25384/sage.8924099
This text is a supplement to the current record

Online_Appendix_Final – Supplemental material for Extending Classification Algorithms to Case-Control Studies
text, January 2019

Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa
Figshare
DOI: 10.25384/sage.8924099.v1
This text is a supplement to the current record

Supplemental_Material – Supplemental material for Extending Classification Algorithms to Case-Control Studies
text, January 2019

Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa
Figshare
DOI: 10.25384/sage.8924111
This text is a supplement to the current record

Supplemental_Material – Supplemental material for Extending Classification Algorithms to Case-Control Studies
text, January 2019

Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa
Figshare
DOI: 10.25384/sage.8924111.v1
This text is a supplement to the current record