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 *

211 related articles for article (PubMed ID: 20838583)

  • 1. An integrative multi-network and multi-classifier approach to predict genetic interactions.
    Pandey G; Zhang B; Chang AN; Myers CL; Zhu J; Kumar V; Schadt EE
    PLoS Comput Biol; 2010 Sep; 6(9):. PubMed ID: 20838583
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting genetic interactions with random walks on biological networks.
    Chipman KC; Singh AK
    BMC Bioinformatics; 2009 Jan; 10():17. PubMed ID: 19138426
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.
    You ZH; Yin Z; Han K; Huang DS; Zhou X
    BMC Bioinformatics; 2010 Jun; 11():343. PubMed ID: 20573270
    [TBL] [Abstract][Full Text] [Related]  

  • 4. MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.
    Dey A; Mudunuri S; Kiran M
    PLoS Comput Biol; 2024 Aug; 20(8):e1012336. PubMed ID: 39186799
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Modeling synthetic lethality.
    Le Meur N; Gentleman R
    Genome Biol; 2008; 9(9):R135. PubMed ID: 18789146
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Filtering high-throughput protein-protein interaction data using a combination of genomic features.
    Patil A; Nakamura H
    BMC Bioinformatics; 2005 Apr; 6():100. PubMed ID: 15833142
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Combining multisource information through functional-annotation-based weighting: gene function prediction in yeast.
    Ray SS; Bandyopadhyay S; Pal SK
    IEEE Trans Biomed Eng; 2009 Feb; 56(2):229-36. PubMed ID: 19272921
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting quantitative genetic interactions by means of sequential matrix approximation.
    Järvinen AP; Hiissa J; Elo LL; Aittokallio T
    PLoS One; 2008 Sep; 3(9):e3284. PubMed ID: 18818762
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting gene phenotype by multi-label multi-class model based on essential functional features.
    Chen L; Li Z; Zeng T; Zhang YH; Li H; Huang T; Cai YD
    Mol Genet Genomics; 2021 Jul; 296(4):905-918. PubMed ID: 33914130
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Causal inference of regulator-target pairs by gene mapping of expression phenotypes.
    Kulp DC; Jagalur M
    BMC Genomics; 2006 May; 7():125. PubMed ID: 16719927
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.
    Wu WS; Lai FJ; Tu BW; Chang DT
    Database (Oxford); 2016; 2016():. PubMed ID: 27242036
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions.
    Li B; Cao W; Zhou J; Luo F
    BMC Syst Biol; 2011 May; 5():73. PubMed ID: 21586150
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Paths Through the Yeast Regulatory Network in Different Physiological States.
    Lesk AM; Konagurthu AS
    J Mol Biol; 2021 Oct; 433(21):167181. PubMed ID: 34339724
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting the effects of copy-number variation in double and triple mutant combinations.
    Carter GW; Hays M; Li S; Galitski T
    Pac Symp Biocomput; 2012; ():19-30. PubMed ID: 22174259
    [TBL] [Abstract][Full Text] [Related]  

  • 15. In silico prediction of synthetic lethality by meta-analysis of genetic interactions, functions, and pathways in yeast and human cancer.
    Wu M; Li X; Zhang F; Li X; Kwoh CK; Zheng J
    Cancer Inform; 2014; 13(Suppl 3):71-80. PubMed ID: 25452682
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Mapping genetically compensatory pathways from synthetic lethal interactions in yeast.
    Ma X; Tarone AM; Li W
    PLoS One; 2008 Apr; 3(4):e1922. PubMed ID: 18398455
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Mining protein networks for synthetic genetic interactions.
    Paladugu SR; Zhao S; Ray A; Raval A
    BMC Bioinformatics; 2008 Oct; 9():426. PubMed ID: 18844977
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators.
    Padi M; Quackenbush J
    BMC Syst Biol; 2015 Nov; 9():80. PubMed ID: 26576632
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Genetic Interaction Motif Finding by expectation maximization--a novel statistical model for inferring gene modules from synthetic lethality.
    Qi Y; Ye P; Bader JS
    BMC Bioinformatics; 2005 Dec; 6():288. PubMed ID: 16332255
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis.
    Ignatius Pang CN; Goel A; Wilkins MR
    J Proteome Res; 2018 Mar; 17(3):1014-1030. PubMed ID: 29392949
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.