140 related articles for article (PubMed ID: 25568281)
1. Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.
Zhang Q; Abel H; Wells A; Lenzini P; Gomez F; Province MA; Templeton AA; Weinstock GM; Salzman NH; Borecki IB
Bioinformatics; 2015 May; 31(10):1607-13. PubMed ID: 25568281
[TBL] [Abstract][Full Text] [Related]
2. Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso.
Kamkar I; Gupta SK; Phung D; Venkatesh S
J Biomed Inform; 2015 Feb; 53():277-90. PubMed ID: 25500636
[TBL] [Abstract][Full Text] [Related]
3. SuRF: A new method for sparse variable selection, with application in microbiome data analysis.
Liu L; Gu H; Van Limbergen J; Kenney T
Stat Med; 2021 Feb; 40(4):897-919. PubMed ID: 33219557
[TBL] [Abstract][Full Text] [Related]
4. Variable selection for multiply-imputed data with application to dioxin exposure study.
Chen Q; Wang S
Stat Med; 2013 Sep; 32(21):3646-59. PubMed ID: 23526243
[TBL] [Abstract][Full Text] [Related]
5. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.
Selker HP; Griffith JL; Patil S; Long WJ; D'Agostino RB
J Investig Med; 1995 Oct; 43(5):468-76. PubMed ID: 8528758
[TBL] [Abstract][Full Text] [Related]
6. A model for paired-multinomial data and its application to analysis of data on a taxonomic tree.
Shi P; Li H
Biometrics; 2017 Dec; 73(4):1266-1278. PubMed ID: 28369713
[TBL] [Abstract][Full Text] [Related]
7. Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data.
Garcia TP; Müller S; Carroll RJ; Walzem RL
Bioinformatics; 2014 Mar; 30(6):831-7. PubMed ID: 24162467
[TBL] [Abstract][Full Text] [Related]
8. Variance Component Selection With Applications to Microbiome Taxonomic Data.
Zhai J; Kim J; Knox KS; Twigg HL; Zhou H; Zhou JJ
Front Microbiol; 2018; 9():509. PubMed ID: 29643839
[TBL] [Abstract][Full Text] [Related]
9. Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.
Xu L; Paterson AD; Turpin W; Xu W
PLoS One; 2015; 10(7):e0129606. PubMed ID: 26148172
[TBL] [Abstract][Full Text] [Related]
10. Examining the use of decision trees in population health surveillance research: an application to youth mental health survey data in the COMPASS study.
Battista K; Diao L; Patte KA; Dubin JA; Leatherdale ST
Health Promot Chronic Dis Prev Can; 2023 Feb; 43(2):73-86. PubMed ID: 36794824
[TBL] [Abstract][Full Text] [Related]
11. An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.
Wadsworth WD; Argiento R; Guindani M; Galloway-Pena J; Shelburne SA; Vannucci M
BMC Bioinformatics; 2017 Feb; 18(1):94. PubMed ID: 28178947
[TBL] [Abstract][Full Text] [Related]
12. MiRKAT-S: a community-level test of association between the microbiota and survival times.
Plantinga A; Zhan X; Zhao N; Chen J; Jenq RR; Wu MC
Microbiome; 2017 Feb; 5(1):17. PubMed ID: 28179014
[TBL] [Abstract][Full Text] [Related]
13. Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations.
Torbati ME; Mitreva M; Gopalakrishnan V
Data (Basel); 2016 Dec; 1(3):. PubMed ID: 28239609
[TBL] [Abstract][Full Text] [Related]
14. Bayesian compositional regression with structured priors for microbiome feature selection.
Zhang L; Shi Y; Jenq RR; Do KA; Peterson CB
Biometrics; 2021 Sep; 77(3):824-838. PubMed ID: 32686846
[TBL] [Abstract][Full Text] [Related]
15. A logistic normal multinomial regression model for microbiome compositional data analysis.
Xia F; Chen J; Fung WK; Li H
Biometrics; 2013 Dec; 69(4):1053-63. PubMed ID: 24128059
[TBL] [Abstract][Full Text] [Related]
16. Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies.
Chen J; Chen W; Zhao N; Wu MC; Schaid DJ
Genet Epidemiol; 2016 Jan; 40(1):5-19. PubMed ID: 26643881
[TBL] [Abstract][Full Text] [Related]
17. Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.
Colombet I; Ruelland A; Chatellier G; Gueyffier F; Degoulet P; Jaulent MC
Proc AMIA Symp; 2000; ():156-60. PubMed ID: 11079864
[TBL] [Abstract][Full Text] [Related]
18. Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.
Coull BA; Bobb JF; Wellenius GA; Kioumourtzoglou MA; Mittleman MA; Koutrakis P; Godleski JJ
Res Rep Health Eff Inst; 2015 Jun; (183 Pt 1-2):5-50. PubMed ID: 26333238
[TBL] [Abstract][Full Text] [Related]
19. Use of the l1 norm for selection of sparse parameter sets that accurately predict drug response phenotype from viral genetic sequences.
Matthew R; Banjevic M; Chan AS; Myers L; Wolkowicz R; Haberer J; Singer J
AMIA Annu Symp Proc; 2005; 2005():505-9. PubMed ID: 16779091
[TBL] [Abstract][Full Text] [Related]
20. Logistic Regression-Based Trichotomous Classification Tree and Its Application in Medical Diagnosis.
Zhu Y; Fang J
Med Decis Making; 2016 Nov; 36(8):973-89. PubMed ID: 26790453
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
