These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

241 related articles for article (PubMed ID: 29887230)

  • 41. How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking.
    Wang KM; Chen KH; Hernanda CA; Tseng SH; Wang KJ
    Int J Environ Res Public Health; 2022 Jul; 19(14):. PubMed ID: 35886298
    [TBL] [Abstract][Full Text] [Related]  

  • 42. A comparison of rule-based and machine learning approaches for classifying patient portal messages.
    Cronin RM; Fabbri D; Denny JC; Rosenbloom ST; Jackson GP
    Int J Med Inform; 2017 Sep; 105():110-120. PubMed ID: 28750904
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Comparing traditional modeling approaches versus predictive analytics methods for predicting multiple sclerosis relapse.
    Walsh K; Shah R; Armstrong JK; Moore ES; Oliver BJ
    Mult Scler Relat Disord; 2022 Jan; 57():103330. PubMed ID: 35158444
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Comparison of subset selection methods in linear regression in the context of health-related quality of life and substance abuse in Russia.
    Morozova O; Levina O; Uusküla A; Heimer R
    BMC Med Res Methodol; 2015 Aug; 15():71. PubMed ID: 26319135
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Efficient learning from big data for cancer risk modeling: A case study with melanoma.
    Richter AN; Khoshgoftaar TM
    Comput Biol Med; 2019 Jul; 110():29-39. PubMed ID: 31112896
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Block Forests: random forests for blocks of clinical and omics covariate data.
    Hornung R; Wright MN
    BMC Bioinformatics; 2019 Jun; 20(1):358. PubMed ID: 31248362
    [TBL] [Abstract][Full Text] [Related]  

  • 47. A learning-based material decomposition pipeline for multi-energy x-ray imaging.
    Lu Y; Kowarschik M; Huang X; Xia Y; Choi JH; Chen S; Hu S; Ren Q; Fahrig R; Hornegger J; Maier A
    Med Phys; 2019 Feb; 46(2):689-703. PubMed ID: 30508253
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.
    Alghamdi M; Al-Mallah M; Keteyian S; Brawner C; Ehrman J; Sakr S
    PLoS One; 2017; 12(7):e0179805. PubMed ID: 28738059
    [TBL] [Abstract][Full Text] [Related]  

  • 49. A random forest based biomarker discovery and power analysis framework for diagnostics research.
    Acharjee A; Larkman J; Xu Y; Cardoso VR; Gkoutos GV
    BMC Med Genomics; 2020 Nov; 13(1):178. PubMed ID: 33228632
    [TBL] [Abstract][Full Text] [Related]  

  • 50. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.
    Naghibi SA; Pourghasemi HR; Dixon B
    Environ Monit Assess; 2016 Jan; 188(1):44. PubMed ID: 26687087
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.
    Marucci-Wellman HR; Corns HL; Lehto MR
    Accid Anal Prev; 2017 Jan; 98():359-371. PubMed ID: 27863339
    [TBL] [Abstract][Full Text] [Related]  

  • 52. An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records.
    Kavuluru R; Rios A; Lu Y
    Artif Intell Med; 2015 Oct; 65(2):155-66. PubMed ID: 26054428
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms.
    Kryshchyshyn A; Devinyak O; Kaminskyy D; Grellier P; Lesyk R
    Mol Inform; 2018 May; 37(5):e1700078. PubMed ID: 29134756
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM
    Xu Y; Ho HC; Wong MS; Deng C; Shi Y; Chan TC; Knudby A
    Environ Pollut; 2018 Nov; 242(Pt B):1417-1426. PubMed ID: 30142557
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Knockoff boosted tree for model-free variable selection.
    Jiang T; Li Y; Motsinger-Reif AA
    Bioinformatics; 2021 May; 37(7):976-983. PubMed ID: 32966559
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Improving random forest predictions in small datasets from two-phase sampling designs.
    Han S; Williamson BD; Fong Y
    BMC Med Inform Decis Mak; 2021 Nov; 21(1):322. PubMed ID: 34809631
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches.
    Jones A; Costa AP; Pesevski A; McNicholas PD
    PLoS One; 2018; 13(11):e0206662. PubMed ID: 30383850
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach.
    Mansoor H; Elgendy IY; Segal R; Bavry AA; Bian J
    Heart Lung; 2017; 46(6):405-411. PubMed ID: 28992993
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.
    Sakr S; Elshawi R; Ahmed AM; Qureshi WT; Brawner CA; Keteyian SJ; Blaha MJ; Al-Mallah MH
    BMC Med Inform Decis Mak; 2017 Dec; 17(1):174. PubMed ID: 29258510
    [TBL] [Abstract][Full Text] [Related]  

  • 60. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.
    Yang JH; Cheng CH; Chan CP
    Comput Intell Neurosci; 2017; 2017():8734214. PubMed ID: 29250110
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 13.