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For: Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res. 2018;24:1248-1259. [PMID: 28982688 DOI: 10.1158/1078-0432.ccr-17-0853] [Cited by in Crossref: 272] [Cited by in F6Publishing: 164] [Article Influence: 54.4] [Reference Citation Analysis]
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14 Bhattacharjee A, Bayzid MS. Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices. BMC Genomics 2020;21:497. [PMID: 32689946 DOI: 10.1186/s12864-020-06892-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
15 Li L, Xia S, Shi X, Chen X, Shang D. The novel immune-related genes predict the prognosis of patients with hepatocellular carcinoma. Sci Rep 2021;11:10728. [PMID: 34021184 DOI: 10.1038/s41598-021-89747-7] [Reference Citation Analysis]
16 Wang H, Yu S, Cai Q, Ma D, Yang L, Zhao J, Jiang L, Zhang X, Yu Z. The Prognostic Model Based on Tumor Cell Evolution Trajectory Reveals a Different Risk Group of Hepatocellular Carcinoma. Front Cell Dev Biol 2021;9:737723. [PMID: 34660596 DOI: 10.3389/fcell.2021.737723] [Reference Citation Analysis]
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18 Ning W, Acharya A, Sun Z, Ogbuehi AC, Li C, Hua S, Ou Q, Zeng M, Liu X, Deng Y, Haak R, Ziebolz D, Schmalz G, Pelekos G, Wang Y, Hu X. Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis. Front Genet 2021;12:648329. [PMID: 33777111 DOI: 10.3389/fgene.2021.648329] [Reference Citation Analysis]
19 Choi H. Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions. Nucl Med Mol Imaging. 2018;52:109-118. [PMID: 29662559 DOI: 10.1007/s13139-017-0504-7] [Cited by in Crossref: 28] [Cited by in F6Publishing: 22] [Article Influence: 5.6] [Reference Citation Analysis]
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21 Huang Z, Johnson TS, Han Z, Helm B, Cao S, Zhang C, Salama P, Rizkalla M, Yu CY, Cheng J, Xiang S, Zhan X, Zhang J, Huang K. Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations. BMC Med Genomics 2020;13:41. [PMID: 32241264 DOI: 10.1186/s12920-020-0686-1] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 7.5] [Reference Citation Analysis]
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23 Zhu C, Zhang L, Zhao S, Dai W, Xu Y, Zhang Y, Zheng H, Sheng W, Xu Y. UPF1 promotes chemoresistance to oxaliplatin through regulation of TOP2A activity and maintenance of stemness in colorectal cancer. Cell Death Dis 2021;12:519. [PMID: 34021129 DOI: 10.1038/s41419-021-03798-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Li S, Mai Z, Gu W, Ogbuehi AC, Acharya A, Pelekos G, Ning W, Liu X, Deng Y, Li H, Lethaus B, Savkovic V, Zimmerer R, Ziebolz D, Schmalz G, Wang H, Xiao H, Zhao J. Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach. Front Cell Dev Biol 2021;9:687245. [PMID: 34422810 DOI: 10.3389/fcell.2021.687245] [Reference Citation Analysis]
25 Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel). 2020;12. [PMID: 33256107 DOI: 10.3390/cancers12123532] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
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27 Chen B, Garmire L, Calvisi DF, Chua MS, Kelley RK, Chen X. Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2020;17:238-51. [PMID: 31900465 DOI: 10.1038/s41575-019-0240-9] [Cited by in Crossref: 29] [Cited by in F6Publishing: 26] [Article Influence: 14.5] [Reference Citation Analysis]
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29 Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front Pharmacol 2020;11:1177. [PMID: 32903628 DOI: 10.3389/fphar.2020.01177] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
30 Hersh CP, Adcock IM, Celedón JC, Cho MH, Christiani DC, Himes BE, Kaminski N, Mathias RA, Meyers DA, Quackenbush J, Redline S, Steiling KA, Tabor HK, Tobin MD, Wurfel MM, Yang IV, Koppelman GH. High-Throughput Sequencing in Respiratory, Critical Care, and Sleep Medicine Research. An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2019;16:1-16. [PMID: 30592451 DOI: 10.1513/AnnalsATS.201810-716WS] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
31 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
32 Chen G, Wang R, Zhang C, Gui L, Xue Y, Ren X, Li Z, Wang S, Zhang Z, Zhao J, Zhang H, Yao C, Wang J, Liu J. Integration of pre-surgical blood test results predict microvascular invasion risk in hepatocellular carcinoma. Comput Struct Biotechnol J 2021;19:826-34. [PMID: 33598098 DOI: 10.1016/j.csbj.2021.01.014] [Reference Citation Analysis]
33 Yuan X, Yi M, Dong B, Chu Q, Wu K. Prognostic significance of KRT19 in Lung Squamous Cancer. J Cancer 2021;12:1240-8. [PMID: 33442422 DOI: 10.7150/jca.51179] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
34 Tufail AB, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, Khan R. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med 2021;2021:9025470. [PMID: 34754327 DOI: 10.1155/2021/9025470] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
35 Mirza B, Wang W, Wang J, Choi H, Chung NC, Ping P. Machine Learning and Integrative Analysis of Biomedical Big Data. Genes (Basel) 2019;10:E87. [PMID: 30696086 DOI: 10.3390/genes10020087] [Cited by in Crossref: 67] [Cited by in F6Publishing: 50] [Article Influence: 22.3] [Reference Citation Analysis]
36 Ye C, Wang H, Li Z, Xia C, Yuan S, Yan R, Yang X, Ma T, Wen X, Yang D. Comprehensive data analysis of genomics, epigenomics, and transcriptomics to identify specific biomolecular markers for prostate adenocarcinoma. Transl Androl Urol 2021;10:3030-45. [PMID: 34430406 DOI: 10.21037/tau-21-576] [Reference Citation Analysis]
37 Chen Q, Hu Z, Zhang X, Wei Z, Fu H, Yang D, Cai Q. A four-lncRNA signature for predicting prognosis of recurrence patients with gastric cancer. Open Med (Wars) 2021;16:540-52. [PMID: 33869776 DOI: 10.1515/med-2021-0241] [Reference Citation Analysis]
38 Baek B, Lee H. Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data. Sci Rep 2020;10:18951. [PMID: 33144687 DOI: 10.1038/s41598-020-76025-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
39 Dincer AB, Janizek JD, Lee SI. Adversarial deconfounding autoencoder for learning robust gene expression embeddings. Bioinformatics 2020;36:i573-82. [PMID: 33381842 DOI: 10.1093/bioinformatics/btaa796] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
40 Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021;19:3735-46. [PMID: 34285775 DOI: 10.1016/j.csbj.2021.06.030] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
41 Zhang X, Xing Y, Sun K, Guo Y. OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data. Cancers (Basel) 2021;13:3047. [PMID: 34207255 DOI: 10.3390/cancers13123047] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
42 Arora C, Kaur D, Lathwal A, Raghava GPS. Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data. Heliyon 2020;6:e04811. [PMID: 32913910 DOI: 10.1016/j.heliyon.2020.e04811] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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44 Mochida K, Nishii R, Hirayama T. Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits. Plant Cell Physiol 2020;61:1408-18. [PMID: 32392328 DOI: 10.1093/pcp/pcaa064] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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46 Hamamoto R, Komatsu M, Takasawa K, Asada K, Kaneko S. Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine. Biomolecules. 2019;10:62. [PMID: 31905969 DOI: 10.3390/biom10010062] [Cited by in Crossref: 22] [Cited by in F6Publishing: 20] [Article Influence: 7.3] [Reference Citation Analysis]
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57 Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1(1): 5-11 [DOI: 10.35712/aig.v1.i1.5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
58 Li X, Li Y, Liang Y, Hu R, Xu W, Liu Y. Plasma Targeted Metabolomics Analysis for Amino Acids and Acylcarnitines in Patients with Prediabetes, Type 2 Diabetes Mellitus, and Diabetic Vascular Complications. Diabetes Metab J 2021;45:195-208. [PMID: 33685035 DOI: 10.4093/dmj.2019.0209] [Reference Citation Analysis]
59 Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2(2): 42-55 [DOI: 10.35712/aig.v2.i2.42] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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61 Guo W, Wang H, Chen P, Shen X, Zhang B, Liu J, Peng H, Xiao X. Identification and Characterization of Multiple Myeloma Stem Cell-Like Cells. Cancers (Basel) 2021;13:3523. [PMID: 34298738 DOI: 10.3390/cancers13143523] [Reference Citation Analysis]
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63 Matsuo K, Purushotham S, Jiang B, Mandelbaum RS, Takiuchi T, Liu Y, Roman LD. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model. Am J Obstet Gynecol 2019;220:381.e1-381.e14. [PMID: 30582927 DOI: 10.1016/j.ajog.2018.12.030] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 5.0] [Reference Citation Analysis]
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