BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

268 related articles for article (PubMed ID: 23874456)

  • 1. The role of balanced training and testing data sets for binary classifiers in bioinformatics.
    Wei Q; Dunbrack RL
    PLoS One; 2013; 8(7):e67863. PubMed ID: 23874456
    [TBL] [Abstract][Full Text] [Related]  

  • 2. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants.
    Petrini A; Mesiti M; Schubach M; Frasca M; Danis D; Re M; Grossi G; Cappelletti L; Castrignanò T; Robinson PN; Valentini G
    Gigascience; 2020 May; 9(5):. PubMed ID: 32444882
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Imbalanced class learning in epigenetics.
    Haque MM; Skinner MK; Holder LB
    J Comput Biol; 2014 Jul; 21(7):492-507. PubMed ID: 24798423
    [TBL] [Abstract][Full Text] [Related]  

  • 4. GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning.
    Esposito C; Landrum GA; Schneider N; Stiefl N; Riniker S
    J Chem Inf Model; 2021 Jun; 61(6):2623-2640. PubMed ID: 34100609
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Class-imbalanced classifiers for high-dimensional data.
    Lin WJ; Chen JJ
    Brief Bioinform; 2013 Jan; 14(1):13-26. PubMed ID: 22408190
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An active learning based classification strategy for the minority class problem: application to histopathology annotation.
    Doyle S; Monaco J; Feldman M; Tomaszewski J; Madabhushi A
    BMC Bioinformatics; 2011 Oct; 12():424. PubMed ID: 22034914
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data.
    Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O'Byrne J; Jerbi K
    Neuroimage; 2023 Aug; 277():120253. PubMed ID: 37385392
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase.
    Wei Q; Wang L; Wang Q; Kruger WD; Dunbrack RL
    Proteins; 2010 Jul; 78(9):2058-74. PubMed ID: 20455263
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction.
    Tao Y; Zhang Y; Jiang B
    BMC Med Genomics; 2020 Oct; 13(Suppl 10):152. PubMed ID: 33087117
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A new disease-specific machine learning approach for the prediction of cancer-causing missense variants.
    Capriotti E; Altman RB
    Genomics; 2011 Oct; 98(4):310-7. PubMed ID: 21763417
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.
    Barenboim M; Masso M; Vaisman II; Jamison DC
    Proteins; 2008 Jun; 71(4):1930-9. PubMed ID: 18186470
    [TBL] [Abstract][Full Text] [Related]  

  • 12. usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme.
    Tang X; Zhang T; Cheng N; Wang H; Zheng CH; Xia J; Zhang T
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33866367
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Structure-activity relationship-based chemical classification of highly imbalanced Tox21 datasets.
    Idakwo G; Thangapandian S; Luttrell J; Li Y; Wang N; Zhou Z; Hong H; Yang B; Zhang C; Gong P
    J Cheminform; 2020 Oct; 12(1):66. PubMed ID: 33372637
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ensemble learning with active example selection for imbalanced biomedical data classification.
    Oh S; Lee MS; Zhang BT
    IEEE/ACM Trans Comput Biol Bioinform; 2011; 8(2):316-25. PubMed ID: 20876935
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Conversion of adverse data corpus to shrewd output using sampling metrics.
    Ashraf S; Saleem S; Ahmed T; Aslam Z; Muhammad D
    Vis Comput Ind Biomed Art; 2020 Aug; 3(1):19. PubMed ID: 32779031
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.
    Tarimo CS; Bhuyan SS; Zhao Y; Ren W; Mohammed A; Li Q; Gardner M; Mahande MJ; Wang Y; Wu J
    BMC Pregnancy Childbirth; 2022 Apr; 22(1):275. PubMed ID: 35365129
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improving classification of mature microRNA by solving class imbalance problem.
    Wang Y; Li X; Tao B
    Sci Rep; 2016 May; 6():25941. PubMed ID: 27181057
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.
    Qu W; Balki I; Mendez M; Valen J; Levman J; Tyrrell PN
    Int J Comput Assist Radiol Surg; 2020 Dec; 15(12):2041-2048. PubMed ID: 32965624
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric.
    Boughorbel S; Jarray F; El-Anbari M
    PLoS One; 2017; 12(6):e0177678. PubMed ID: 28574989
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy.
    García S; Herrera F
    Evol Comput; 2009; 17(3):275-306. PubMed ID: 19708770
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.