153 related articles for article (PubMed ID: 31288637)
1. Deep learning with evolutionary and genomic profiles for identifying cancer subtypes.
Lin CY; Ruan P; Li R; Yang JM; See S; Song J; Akutsu T
J Bioinform Comput Biol; 2019 Jun; 17(3):1940005. PubMed ID: 31288637
[TBL] [Abstract][Full Text] [Related]
2. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.
Lu X; Li X; Liu P; Qian X; Miao Q; Peng S
Molecules; 2018 Jan; 23(2):. PubMed ID: 29364829
[TBL] [Abstract][Full Text] [Related]
3. Convolutional neural network models for cancer type prediction based on gene expression.
Mostavi M; Chiu YC; Huang Y; Chen Y
BMC Med Genomics; 2020 Apr; 13(Suppl 5):44. PubMed ID: 32241303
[TBL] [Abstract][Full Text] [Related]
4. Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes.
Samarasinghe S; Chaiboonchoe A
Methods Mol Biol; 2015; 1260():285-317. PubMed ID: 25502389
[TBL] [Abstract][Full Text] [Related]
5. An Integrative Approach for Identifying Network Biomarkers of Breast Cancer Subtypes Using Genomic, Interactomic, and Transcriptomic Data.
Firoozbakht F; Rezaeian I; D'agnillo M; Porter L; Rueda L; Ngom A
J Comput Biol; 2017 Aug; 24(8):756-766. PubMed ID: 28650678
[TBL] [Abstract][Full Text] [Related]
6. Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer.
Lee G; Bang L; Kim SY; Kim D; Sohn KA
BMC Med Genomics; 2017 May; 10(Suppl 1):28. PubMed ID: 28589855
[TBL] [Abstract][Full Text] [Related]
7. Deep learning identified glioblastoma subtypes based on internal genomic expression ranks.
Mao XG; Xue XY; Wang L; Lin W; Zhang X
BMC Cancer; 2022 Jan; 22(1):86. PubMed ID: 35057766
[TBL] [Abstract][Full Text] [Related]
8. MicroRNA signatures highlight new breast cancer subtypes.
Bhattacharyya M; Nath J; Bandyopadhyay S
Gene; 2015 Feb; 556(2):192-8. PubMed ID: 25485717
[TBL] [Abstract][Full Text] [Related]
9. A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq.
Sundaramurthy G; Eghbalnia HR
Comput Biol Med; 2015 Dec; 67():29-40. PubMed ID: 26492320
[TBL] [Abstract][Full Text] [Related]
10. In silico markers: an evolutionary and statistical approach to select informative genes of human breast cancer subtypes.
Bhowmick SS; Bhattacharjee D; Rato L
Genes Genomics; 2019 Dec; 41(12):1371-1382. PubMed ID: 31004329
[TBL] [Abstract][Full Text] [Related]
11. Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.
Pyman B; Sedghi A; Azizi S; Tyryshkin K; Renwick N; Mousavi P
Pac Symp Biocomput; 2019; 24():160-171. PubMed ID: 30864319
[TBL] [Abstract][Full Text] [Related]
12. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.
Noorbakhsh J; Farahmand S; Foroughi Pour A; Namburi S; Caruana D; Rimm D; Soltanieh-Ha M; Zarringhalam K; Chuang JH
Nat Commun; 2020 Dec; 11(1):6367. PubMed ID: 33311458
[TBL] [Abstract][Full Text] [Related]
13. Identification of Novel Breast Cancer Subtype-Specific Biomarkers by Integrating Genomics Analysis of DNA Copy Number Aberrations and miRNA-mRNA Dual Expression Profiling.
Li D; Xia H; Li ZY; Hua L; Li L
Biomed Res Int; 2015; 2015():746970. PubMed ID: 25961039
[TBL] [Abstract][Full Text] [Related]
14. Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer.
Naorem LD; Muthaiyan M; Venkatesan A
J Cell Biochem; 2019 Apr; 120(4):6154-6167. PubMed ID: 30302816
[TBL] [Abstract][Full Text] [Related]
15. Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.
Zhang T; Zhang L; Payne PRO; Li F
Methods Mol Biol; 2021; 2194():223-238. PubMed ID: 32926369
[TBL] [Abstract][Full Text] [Related]
16. Identifying common transcriptome signatures of cancer by interpreting deep learning models.
Jha A; Quesnel-Vallières M; Wang D; Thomas-Tikhonenko A; Lynch KW; Barash Y
Genome Biol; 2022 May; 23(1):117. PubMed ID: 35581644
[TBL] [Abstract][Full Text] [Related]
17. Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders.
Smolander J; Dehmer M; Emmert-Streib F
FEBS Open Bio; 2019 Jul; 9(7):1232-1248. PubMed ID: 31074948
[TBL] [Abstract][Full Text] [Related]
18. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.
Guo Y; Liu S; Li Z; Shang X
BMC Bioinformatics; 2018 Apr; 19(Suppl 5):118. PubMed ID: 29671390
[TBL] [Abstract][Full Text] [Related]
19. DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.
Azarkhalili B; Saberi A; Chitsaz H; Sharifi-Zarchi A
Sci Rep; 2019 Nov; 9(1):16526. PubMed ID: 31712594
[TBL] [Abstract][Full Text] [Related]
20. Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups.
Netanely D; Avraham A; Ben-Baruch A; Evron E; Shamir R
Breast Cancer Res; 2016 Jul; 18(1):74. PubMed ID: 27386846
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
