393 related articles for article (PubMed ID: 33976213)
1. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
Lewis JE; Kemp ML
Nat Commun; 2021 May; 12(1):2700. PubMed ID: 33976213
[TBL] [Abstract][Full Text] [Related]
2. A meta-learning approach to improving radiation response prediction in cancers.
Zhang Y; Qiu L; Ren Y; Cheng Z; Li L; Yao S; Zhang C; Luo Z; Lu H
Comput Biol Med; 2022 Nov; 150():106163. PubMed ID: 37070625
[TBL] [Abstract][Full Text] [Related]
3. Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors.
Lewis JE; Forshaw TE; Boothman DA; Furdui CM; Kemp ML
Cell Syst; 2021 Jan; 12(1):68-81.e11. PubMed ID: 33476554
[TBL] [Abstract][Full Text] [Related]
4. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.
Han Y; Yang J; Qian X; Cheng WC; Liu SH; Hua X; Zhou L; Yang Y; Wu Q; Liu P; Lu Y
Nucleic Acids Res; 2019 May; 47(8):e45. PubMed ID: 30773592
[TBL] [Abstract][Full Text] [Related]
5. DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.
Poirion OB; Jing Z; Chaudhary K; Huang S; Garmire LX
Genome Med; 2021 Jul; 13(1):112. PubMed ID: 34261540
[TBL] [Abstract][Full Text] [Related]
6. Bayesian variable selection with graphical structure learning: Applications in integrative genomics.
Kundu S; Cheng Y; Shin M; Manyam G; Mallick BK; Baladandayuthapani V
PLoS One; 2018; 13(7):e0195070. PubMed ID: 30059495
[TBL] [Abstract][Full Text] [Related]
7. Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy.
Manem VS; Lambie M; Smith I; Smirnov P; Kofia V; Freeman M; Koritzinsky M; Abazeed ME; Haibe-Kains B; Bratman SV
Cancer Res; 2019 Dec; 79(24):6227-6237. PubMed ID: 31558563
[TBL] [Abstract][Full Text] [Related]
8. Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE).
Ma T; Zhang A
BMC Genomics; 2019 Dec; 20(Suppl 11):944. PubMed ID: 31856727
[TBL] [Abstract][Full Text] [Related]
9. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods.
Magazzù G; Zampieri G; Angione C
Comput Biol Med; 2022 Dec; 151(Pt A):106244. PubMed ID: 36343407
[TBL] [Abstract][Full Text] [Related]
10. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.
Deist TM; Dankers FJWM; Valdes G; Wijsman R; Hsu IC; Oberije C; Lustberg T; van Soest J; Hoebers F; Jochems A; El Naqa I; Wee L; Morin O; Raleigh DR; Bots W; Kaanders JH; Belderbos J; Kwint M; Solberg T; Monshouwer R; Bussink J; Dekker A; Lambin P
Med Phys; 2018 Jul; 45(7):3449-3459. PubMed ID: 29763967
[TBL] [Abstract][Full Text] [Related]
11. Molecular determinants of radiosensitivity in normal and tumor tissue: A bioinformatic approach.
Pavlopoulou A; Bagos PG; Koutsandrea V; Georgakilas AG
Cancer Lett; 2017 Sep; 403():37-47. PubMed ID: 28619524
[TBL] [Abstract][Full Text] [Related]
12. Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.
Rahman MH; Rana HK; Peng S; Hu X; Chen C; Quinn JMW; Moni MA
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33406529
[TBL] [Abstract][Full Text] [Related]
13. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.
Cantini L; Zakeri P; Hernandez C; Naldi A; Thieffry D; Remy E; Baudot A
Nat Commun; 2021 Jan; 12(1):124. PubMed ID: 33402734
[TBL] [Abstract][Full Text] [Related]
14. A denoised multi-omics integration framework for cancer subtype classification and survival prediction.
Pang J; Liang B; Ding R; Yan Q; Chen R; Xu J
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37594302
[TBL] [Abstract][Full Text] [Related]
15. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
Kim D; Li R; Dudek SM; Ritchie MD
J Biomed Inform; 2015 Aug; 56():220-8. PubMed ID: 26048077
[TBL] [Abstract][Full Text] [Related]
16. Development and validation of genomic predictors of radiation sensitivity using preclinical data.
Manem VSK
BMC Cancer; 2021 Aug; 21(1):937. PubMed ID: 34416855
[TBL] [Abstract][Full Text] [Related]
17. Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus.
Cai M; Li L
BMC Med Genomics; 2017 Dec; 10(Suppl 4):75. PubMed ID: 29322925
[TBL] [Abstract][Full Text] [Related]
18. Bidirectional deep neural networks to integrate RNA and DNA data for predicting outcome for patients with hepatocellular carcinoma.
Huang G; Wang C; Fu X
Future Oncol; 2021 Nov; 17(33):4481-4495. PubMed ID: 34374301
[TBL] [Abstract][Full Text] [Related]
19. A radiosensitivity gene signature and PD-L1 status predict clinical outcome of patients with invasive breast carcinoma in The Cancer Genome Atlas (TCGA) dataset.
Jang BS; Kim IA
Radiother Oncol; 2017 Sep; 124(3):403-410. PubMed ID: 28579282
[TBL] [Abstract][Full Text] [Related]
20. A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.
Vijayakumar S; Magazzù G; Moon P; Occhipinti A; Angione C
Methods Mol Biol; 2022; 2399():87-122. PubMed ID: 35604554
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
