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 *

125 related articles for article (PubMed ID: 27366641)

  • 1. The impact of feature selection on one and two-class classification performance for plant microRNAs.
    Khalifa W; Yousef M; Saçar Demirci MD; Allmer J
    PeerJ; 2016; 4():e2135. PubMed ID: 27366641
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.
    Yousef M; Saçar Demirci MD; Khalifa W; Allmer J
    Adv Bioinformatics; 2016; 2016():5670851. PubMed ID: 27190509
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Delineating the impact of machine learning elements in pre-microRNA detection.
    Saçar Demirci MD; Allmer J
    PeerJ; 2017; 5():e3131. PubMed ID: 28367373
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Cancer survival classification using integrated data sets and intermediate information.
    Kim S; Park T; Kon M
    Artif Intell Med; 2014 Sep; 62(1):23-31. PubMed ID: 24997860
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.
    Li F; Piao M; Piao Y; Li M; Ryu KH
    Osong Public Health Res Perspect; 2014 Oct; 5(5):279-85. PubMed ID: 25389514
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection.
    Lopez-Rincon A; Martinez-Archundia M; Martinez-Ruiz GU; Schoenhuth A; Tonda A
    BMC Bioinformatics; 2019 Sep; 20(1):480. PubMed ID: 31533612
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting novel microRNA: a comprehensive comparison of machine learning approaches.
    Stegmayer G; Di Persia LE; Rubiolo M; Gerard M; Pividori M; Yones C; Bugnon LA; Rodriguez T; Raad J; Milone DH
    Brief Bioinform; 2019 Sep; 20(5):1607-1620. PubMed ID: 29800232
    [TBL] [Abstract][Full Text] [Related]  

  • 8. miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.
    Cui H; Zhai J; Ma C
    PLoS One; 2015; 10(11):e0142753. PubMed ID: 26558614
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Fast and accurate microRNA search using CNN.
    Tang X; Sun Y
    BMC Bioinformatics; 2019 Dec; 20(Suppl 23):646. PubMed ID: 31881831
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.
    Yousef M; Khalifa W; AbedAllah L
    J Integr Bioinform; 2016 Dec; 13(5):304. PubMed ID: 28187418
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Learning from positive examples when the negative class is undetermined--microRNA gene identification.
    Yousef M; Jung S; Showe LC; Showe MK
    Algorithms Mol Biol; 2008 Jan; 3():2. PubMed ID: 18226233
    [TBL] [Abstract][Full Text] [Related]  

  • 12. MicroRNA transcription start site prediction with multi-objective feature selection.
    Bhattacharyya M; Feuerbach L; Bhadra T; Lengauer T; Bandyopadhyay S
    Stat Appl Genet Mol Biol; 2012 Jan; 11(1):Article 6. PubMed ID: 22499686
    [TBL] [Abstract][Full Text] [Related]  

  • 13. High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM.
    Stegmayer G; Yones C; Kamenetzky L; Milone DH
    IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(6):1316-1326. PubMed ID: 27295687
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep neural networks for human microRNA precursor detection.
    Zheng X; Fu X; Wang K; Wang M
    BMC Bioinformatics; 2020 Jan; 21(1):17. PubMed ID: 31931701
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MST-GEN: An Efficient Parameter Selection Method for One-Class Extreme Learning Machine.
    Wang S; Liu Q; Zhu E; Yin J; Zhao W
    IEEE Trans Cybern; 2017 Oct; 47(10):3266-3279. PubMed ID: 28600273
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.
    Xuan P; Guo M; Liu X; Huang Y; Li W; Huang Y
    Bioinformatics; 2011 May; 27(10):1368-76. PubMed ID: 21441575
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Automatic learning of pre-miRNAs from different species.
    O N Lopes Id; Schliep A; de L F de Carvalho AP
    BMC Bioinformatics; 2016 May; 17(1):224. PubMed ID: 27233515
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
    Lee HS; Hong H; Jung DC; Park S; Kim J
    Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of MicroRNA Precursors Using Parsimonious Feature Sets.
    Stepanowsky P; Levy E; Kim J; Jiang X; Ohno-Machado L
    Cancer Inform; 2014; 13(Suppl 1):95-102. PubMed ID: 25392687
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification.
    Lopez-Rincon A; Mendoza-Maldonado L; Martinez-Archundia M; Schönhuth A; Kraneveld AD; Garssen J; Tonda A
    Cancers (Basel); 2020 Jul; 12(7):. PubMed ID: 32635415
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.