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 *

189 related articles for article (PubMed ID: 35507263)

  • 21. Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.
    Del Casale A; Sarli G; Bargagna P; Polidori L; Alcibiade A; Zoppi T; Borro M; Gentile G; Zocchi C; Ferracuti S; Preissner R; Simmaco M; Pompili M
    Curr Neuropharmacol; 2023; 21(12):2395-2408. PubMed ID: 37559539
    [TBL] [Abstract][Full Text] [Related]  

  • 22. How much can deep learning improve prediction of the responses to drugs in cancer cell lines?
    Chen Y; Zhang L
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34529029
    [TBL] [Abstract][Full Text] [Related]  

  • 23. DROEG: a method for cancer drug response prediction based on omics and essential genes integration.
    Wu P; Sun R; Fahira A; Chen Y; Jiangzhou H; Wang K; Yang Q; Dai Y; Pan D; Shi Y; Wang Z
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36715269
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. Deep centroid: a general deep cascade classifier for biomedical omics data classification.
    Xie K; Hou Y; Zhou X
    Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38305432
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Drug Response Prediction by Globally Capturing Drug and Cell Line Information in a Heterogeneous Network.
    Le DH; Pham VH
    J Mol Biol; 2018 Sep; 430(18 Pt A):2993-3004. PubMed ID: 29966608
    [TBL] [Abstract][Full Text] [Related]  

  • 27. A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.
    Ahmadi Moughari F; Eslahchi C
    PLoS One; 2021; 16(4):e0250620. PubMed ID: 33914775
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.
    Miao R; Chen HH; Dang Q; Xia LY; Yang ZY; He MF; Hao ZF; Liang Y
    Pharmacol Res; 2020 Sep; 159():104932. PubMed ID: 32473309
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction.
    Tan X; Yu Y; Duan K; Zhang J; Sun P; Sun H
    Curr Top Med Chem; 2020; 20(21):1858-1867. PubMed ID: 32648840
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A quantile regression forest based method to predict drug response and assess prediction reliability.
    Fang Y; Xu P; Yang J; Qin Y
    PLoS One; 2018; 13(10):e0205155. PubMed ID: 30289891
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Machine learning and feature selection for drug response prediction in precision oncology applications.
    Ali M; Aittokallio T
    Biophys Rev; 2019 Feb; 11(1):31-39. PubMed ID: 30097794
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Looking at the BiG picture: incorporating bipartite graphs in drug response prediction.
    Hostallero DE; Li Y; Emad A
    Bioinformatics; 2022 Jul; 38(14):3609-3620. PubMed ID: 35674359
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A Deep Learning Framework for Predicting Response to Therapy in Cancer.
    Sakellaropoulos T; Vougas K; Narang S; Koinis F; Kotsinas A; Polyzos A; Moss TJ; Piha-Paul S; Zhou H; Kardala E; Damianidou E; Alexopoulos LG; Aifantis I; Townsend PA; Panayiotidis MI; Sfikakis P; Bartek J; Fitzgerald RC; Thanos D; Mills Shaw KR; Petty R; Tsirigos A; Gorgoulis VG
    Cell Rep; 2019 Dec; 29(11):3367-3373.e4. PubMed ID: 31825821
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.
    Malik V; Kalakoti Y; Sundar D
    BMC Genomics; 2021 Mar; 22(1):214. PubMed ID: 33761889
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Predicting breast cancer drug response using a multiple-layer cell line drug response network model.
    Huang S; Hu P; Lakowski TM
    BMC Cancer; 2021 May; 21(1):648. PubMed ID: 34059012
    [TBL] [Abstract][Full Text] [Related]  

  • 36. DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.
    Liu H; Wang F; Yu J; Pan Y; Gong C; Zhang L; Zhang L
    PLoS Comput Biol; 2024 Apr; 20(4):e1012012. PubMed ID: 38574114
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.
    Jang IS; Dienstmann R; Margolin AA; Guinney J
    Pac Symp Biocomput; 2015; 20():32-43. PubMed ID: 25592566
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Prediction of anti-cancer drug response by kernelized multi-task learning.
    Tan M
    Artif Intell Med; 2016 Oct; 73():70-77. PubMed ID: 27926382
    [TBL] [Abstract][Full Text] [Related]  

  • 39. PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.
    Graim K; Friedl V; Houlahan KE; Stuart JM
    Pac Symp Biocomput; 2019; 24():136-147. PubMed ID: 30864317
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model.
    Wei D; Liu C; Zheng X; Li Y
    BMC Bioinformatics; 2019 Jan; 20(1):44. PubMed ID: 30670007
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.