Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

Article  PubMed  Google Scholar 

Asare-Werehene M, Nakka K, Reunov A, Chiu C-T, Lee W-T, Abedini MR, et al. The exosome-mediated autocrine and paracrine actions of plasma gelsolin in ovarian cancer chemoresistance. Oncogene. 2020;39:1600–16.

Article  CAS  PubMed  Google Scholar 

Jayson GC, Kohn EC, Kitchener HC, Ledermann JA. Ovarian cancer. Lancet Lond Engl. 2014;384:1376–88.

Article  Google Scholar 

Li Z-Y, Wang X-L, Dang Y, Zhu X-Z, Zhang Y-H, Cai B-X, et al. Long non-coding RNA UCA1 promotes the progression of paclitaxel resistance in ovarian cancer by regulating the miR-654-5p/SIK2 axis. Eur Rev Med Pharmacol Sci. 2020;24:591–603.

PubMed  Google Scholar 

Tian W, Lei N, Zhou J, Chen M, Guo R, Qin B, et al. Extracellular vesicles in ovarian cancer chemoresistance, metastasis, and immune evasion. Cell Death Dis. 2022;13:64.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Armstrong DK, Bundy B, Wenzel L, Huang HQ, Baergen R, Lele S, et al. Intraperitoneal cisplatin and paclitaxel in ovarian cancer. N Engl J Med. 2006;354:34–43.

Article  CAS  PubMed  Google Scholar 

Bogani G, Rossetti D, Ditto A, Martinelli F, Chiappa V, Mosca L, et al. Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer. J Gynecol Oncol. 2018;29:e66.

Article  PubMed  PubMed Central  Google Scholar 

Kurtz J-E, Pujade-Lauraine E, Oaknin A, Belin L, Leitner K, Cibula D, et al. Atezolizumab combined with bevacizumab and platinum-based therapy for platinum-sensitive ovarian cancer: placebo-controlled randomized phase III ATALANTE/ENGOT-ov29 Trial. J Clin Oncol Off J Am Soc Clin Oncol. 2023;41:4768–78.

Article  CAS  Google Scholar 

Lheureux S, Braunstein M, Oza AM. Epithelial ovarian cancer: evolution of management in the era of precision medicine. CA Cancer J Clin. 2019;69:280–304.

Article  PubMed  Google Scholar 

Funingana I-G, Reinius MAV, Petrillo A, Ang JE, Brenton JD. Can integrative biomarker approaches improve prediction of platinum and PARP inhibitor response in ovarian cancer? Semin Cancer Biol. 2021;77:67–82.

Article  CAS  PubMed  Google Scholar 

Wang Q, Chang Z, Liu X, Wang Y, Feng C, Ping Y, et al. Predictive value of machine learning for platinum chemotherapy responses in ovarian cancer: systematic review and meta-analysis. J Med Internet Res. 2024;26:e48527.

Article  PubMed  PubMed Central  Google Scholar 

Coburn SB, Bray F, Sherman ME, Trabert B. International patterns and trends in ovarian cancer incidence, overall and by histologic subtype. Int J Cancer. 2017;140:2451–60.

Article  CAS  PubMed  PubMed Central  Google Scholar 

ovarian.pdf [Internet]. Available from: https://www2.tri-kobe.org/nccn/guideline/gynecological/english/ovarian.pdf. Accessed 6 May 2024.

Noriega-Rivera R, Rivera-Serrano M, Rabelo-Fernandez RJ, Pérez-Santiago J, Valiyeva F, Vivas-Mejía PE. Upregulation of the long noncoding RNA CASC10 promotes cisplatin resistance in high-grade serous ovarian cancer. Int J Mol Sci. 2022;23:7737.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Golubnitschaja O, Polivka J, Potuznik P, Pesta M, Stetkarova I, Mazurakova A, et al. The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation. EPMA J. 2024;15:1–23.

Article  PubMed  PubMed Central  Google Scholar 

Jia W, Li N, Wang J, Gong X, Ouedraogo SY, Wang Y, et al. Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach. EPMA J. [Internet]. 2024 [cited 2024 Jun 1]; https://doi.org/10.1007/s13167-024-00359-3.

Smokovski I, Steinle N, Behnke A, Bhaskar SMM, Grech G, Richter K, et al. Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position. EPMA J. 2024;15:149–62.

Article  PubMed  PubMed Central  Google Scholar 

Goncharenko V, Bubnov R, Polivka J, Zubor P, Biringer K, Bielik T, et al. Vaginal dryness: individualised patient profiles, risks and mitigating measures. EPMA J. 2019;10:73–9.

Article  PubMed  PubMed Central  Google Scholar 

You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther. 2022;7:156.

Article  PubMed  PubMed Central  Google Scholar 

Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, et al. FDA-approved machine learning algorithms in neuroradiology: a systematic review of the current evidence for approval. Artif Intell Med. 2023;143:102607.

Article  PubMed  Google Scholar 

Ghanat Bari M, Ung CY, Zhang C, Zhu S, Li H. Machine learning-assisted network inference approach to identify a new class of genes that coordinate the functionality of cancer networks. Sci Rep. 2017;7:6993.

Article  PubMed  PubMed Central  Google Scholar 

Muzio G, O’Bray L, Borgwardt K. Biological network analysis with deep learning. Brief Bioinform. 2021;22:1515–30.

Article  PubMed  Google Scholar 

Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell. 2018;173:1581–92.

Article  CAS  PubMed  Google Scholar 

Ramazzotti D, Lal A, Wang B, Batzoglou S, Sidow A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun. 2018;9:4453.

Article  PubMed  PubMed Central  Google Scholar 

Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostat Oxf Engl. 2018;19:71–86.

Google Scholar 

Meng C, Helm D, Frejno M, Kuster B. moCluster: identifying joint patterns across multiple omics data sets [Internet]. ACS Publ Am Chem Soc. 2015. Available from: https://pubs.acs.org/doi/full/10.1021/acs.jproteome.5b00824.  Accessed 11 May 2024.

Brière G, Darbo É, Thébault P, Uricaru R. Consensus clustering applied to multi-omics disease subtyping. BMC Bioinformatics. 2021;22:361.

Article  PubMed  PubMed Central  Google Scholar 

Chalise P, Fridley BL. Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm. PLoS ONE. 2017;12:e0176278.

Article  PubMed  PubMed Central  Google Scholar 

Wu D, Wang D, Zhang MQ, Gu J. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics. 2015;16:1022.

Article  PubMed  PubMed Central  Google Scholar 

Rappoport N, Shamir R. NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics. 2019;35:3348–56.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nguyen H, Shrestha S, Draghici S, Nguyen T. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics. 2019;35:2843–6.

Article  CAS  Google Scholar 

Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11:333–7.

Article  CAS  PubMed  Google Scholar 

Bubnov R, Polivka J, Zubor P, Konieczka K, Golubnitschaja O. “Pre-metastatic niches” in breast cancer: are they created by or prior to the tumour onset? “flammer syndrome” relevance to address the question. EPMA J. 2017;8:141–57.

Article  PubMed  PubMed Central  Google Scholar 

Grech G, Zhan X, Yoo BC, Bubnov R, Hagan S, Danesi R, et al. EPMA position paper in cancer: current overview and future perspectives. EPMA J. 2015;6:9.