BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 33952467)

  • 21. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.
    Mendez KM; Reinke SN; Broadhurst DI
    Metabolomics; 2019 Nov; 15(12):150. PubMed ID: 31728648
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Machine learning models in breast cancer survival prediction.
    Montazeri M; Montazeri M; Montazeri M; Beigzadeh A
    Technol Health Care; 2016; 24(1):31-42. PubMed ID: 26409558
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.
    Yu L; Tao G; Zhu L; Wang G; Li Z; Ye J; Chen Q
    BMC Cancer; 2019 May; 19(1):464. PubMed ID: 31101024
    [TBL] [Abstract][Full Text] [Related]  

  • 24. A community effort to assess and improve drug sensitivity prediction algorithms.
    Costello JC; Heiser LM; Georgii E; Gönen M; Menden MP; Wang NJ; Bansal M; Ammad-ud-din M; Hintsanen P; Khan SA; Mpindi JP; Kallioniemi O; Honkela A; Aittokallio T; Wennerberg K; ; Collins JJ; Gallahan D; Singer D; Saez-Rodriguez J; Kaski S; Gray JW; Stolovitzky G
    Nat Biotechnol; 2014 Dec; 32(12):1202-12. PubMed ID: 24880487
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Network-based drug sensitivity prediction.
    Ahmed KT; Park S; Jiang Q; Yeu Y; Hwang T; Zhang W
    BMC Med Genomics; 2020 Dec; 13(Suppl 11):193. PubMed ID: 33371891
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.
    Sharifi-Noghabi H; Jahangiri-Tazehkand S; Smirnov P; Hon C; Mammoliti A; Nair SK; Mer AS; Ester M; Haibe-Kains B
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34382071
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.
    Menden MP; Iorio F; Garnett M; McDermott U; Benes CH; Ballester PJ; Saez-Rodriguez J
    PLoS One; 2013; 8(4):e61318. PubMed ID: 23646105
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.
    Sakai M; Nakano H; Kawahara D; Tanabe S; Takizawa T; Narita A; Yamada T; Sakai H; Ueda M; Sasamoto R; Kaidu M; Aoyama H; Ishikawa H; Utsunomiya S
    Med Phys; 2021 Mar; 48(3):991-1002. PubMed ID: 33382467
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Development of machine learning models for diagnosis of glaucoma.
    Kim SJ; Cho KJ; Oh S
    PLoS One; 2017; 12(5):e0177726. PubMed ID: 28542342
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Machine learning in medicine: a practical introduction.
    Sidey-Gibbons JAM; Sidey-Gibbons CJ
    BMC Med Res Methodol; 2019 Mar; 19(1):64. PubMed ID: 30890124
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.
    Kaiser TM; Burger PB
    Molecules; 2019 Jun; 24(11):. PubMed ID: 31167452
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm.
    Yang L; Fu B; Li Y; Liu Y; Huang W; Feng S; Xiao L; Sun L; Deng L; Zheng X; Ye F; Bu H
    Comput Methods Programs Biomed; 2020 Aug; 192():105458. PubMed ID: 32302875
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.
    Kim YR; Kim SY
    J Cancer Res Clin Oncol; 2018 Aug; 144(8):1435-1444. PubMed ID: 29802456
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
    Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
    Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.
    Russo DP; Zorn KM; Clark AM; Zhu H; Ekins S
    Mol Pharm; 2018 Oct; 15(10):4361-4370. PubMed ID: 30114914
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment.
    Yi X; Liu Y; Zhou B; Xiang W; Deng A; Fu Y; Zhao Y; Ouyang Q; Liu Y; Sun Z; Zhang K; Li X; Zeng F; Zhou H; Chen BT
    Biomed Pharmacother; 2021 Jan; 133():111013. PubMed ID: 33227705
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.
    Liu Q; Muglia LJ; Huang LF
    Genes (Basel); 2019 Aug; 10(8):. PubMed ID: 31405013
    [TBL] [Abstract][Full Text] [Related]  

  • 38. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.
    Chen Z; Li J; Wei L
    Artif Intell Med; 2007 Oct; 41(2):161-75. PubMed ID: 17851055
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Explainable drug sensitivity prediction through cancer pathway enrichment.
    Tang YC; Gottlieb A
    Sci Rep; 2021 Feb; 11(1):3128. PubMed ID: 33542382
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach.
    Kharangarh S; Sandhu H; Tangadpalliwar S; Garg P
    Comb Chem High Throughput Screen; 2018; 21(8):557-566. PubMed ID: 30360705
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.