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Cited by in F6Publishing
For: Sinkala M, Mulder N, Martin D. Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics. Sci Rep. 2020;10:1212. [PMID: 31988390 DOI: 10.1038/s41598-020-58290-2] [Cited by in Crossref: 18] [Cited by in F6Publishing: 9] [Article Influence: 18.0] [Reference Citation Analysis]
Number Citing Articles
1 Sarno F, Benincasa G, List M, Barabasi AL, Baumbach J, Ciardiello F, Filetti S, Glass K, Loscalzo J, Marchese C, Maron BA, Paci P, Parini P, Petrillo E, Silverman EK, Verrienti A, Altucci L, Napoli C; International Network Medicine Consortium. Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside. Clin Epigenetics 2021;13:66. [PMID: 33785068 DOI: 10.1186/s13148-021-01047-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
2 Sivapalan L, Kocher HM, Ross-Adams H, Chelala C. Molecular profiling of ctDNA in pancreatic cancer: Opportunities and challenges for clinical application. Pancreatology. 2021;21:363-378. [PMID: 33451936 DOI: 10.1016/j.pan.2020.12.017] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Choi YI, Park SJ, Chung JW, Kim KO, Cho JH, Kim YJ, Lee KY, Kim KG, Park DK. Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study.J Clin Med. 2020;9:3427. [PMID: 33114505 DOI: 10.3390/jcm9113427] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Kafita D, Daka V, Nkhoma P, Zulu M, Zulu E, Tembo R, Ngwira Z, Mwaba F, Sinkala M, Munsaka S. High ELF4 expression in human cancers is associated with worse disease outcomes and increased resistance to anticancer drugs. PLoS One 2021;16:e0248984. [PMID: 33836003 DOI: 10.1371/journal.pone.0248984] [Reference Citation Analysis]
5 Yan W, Liu X, Wang Y, Han S, Wang F, Liu X, Xiao F, Hu G. Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling. Front Pharmacol 2020;11:534. [PMID: 32425783 DOI: 10.3389/fphar.2020.00534] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
6 Garbulowski M, Smolinska K, Diamanti K, Pan G, Maqbool K, Feuk L, Komorowski J. Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder. Front Genet 2021;12:618277. [PMID: 33719335 DOI: 10.3389/fgene.2021.618277] [Reference Citation Analysis]
7 Adam RS, Blomberg I, Ten Hoorn S, Bijlsma MF, Vermeulen L. The recurring features of molecular subtypes in distinct gastrointestinal malignancies-A systematic review. Crit Rev Oncol Hematol 2021;164:103428. [PMID: 34284100 DOI: 10.1016/j.critrevonc.2021.103428] [Reference Citation Analysis]
8 Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising. World J Gastroenterol 2021;27:1283-95. [PMID: 33833482 DOI: 10.3748/wjg.v27.i13.1283] [Reference Citation Analysis]
9 Lin H, Xue X, Wang X, Dang S, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. AIG 2020;1:19-29. [DOI: 10.35712/aig.v1.i1.19] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]