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23, 10.1038\u002Fs41422-018-0113-8\nToncheva, 2015\nBhat, 2018, Comprehensive network map of interferon gamma signaling, Journal of cell communication and signaling, 12, 745, 10.1007\u002Fs12079-018-0486-y\nHabib, 2016, Application of r to investigate common gene regulatory network pathway among bipolar disorder and associate diseases, Network Biology, 6, 86\nTang, 2018, Interferon-gamma-mediated osteoimmunology, Front Immunol, 9, 1508, 10.3389\u002Ffimmu.2018.01508\nBarbosa, 2014, Cytokines in bipolar disorder: paving the way for neuroprogression, Neural Plast, 2014, 10.1155\u002F2014\u002F360481\nMilenkovic, 2019, The role of chemokines in the pathophysiology of major depressive disorder, Int J Mol Sci, 20, 2283, 10.3390\u002Fijms20092283\nFigueiredo, 2012, Reconsidering the association between the major histocompatibility complex and bipolar disorder, J Mol Neurosci, 47, 26, 10.1007\u002Fs12031-011-9656-6\nC. Carter, “Toxoplasmosis and polygenic disease susceptibility genes: extensive toxoplasma gondii host\u002Fpathogen interactome enrichment in nine psychiatric or neurological disorders,” Journal of pathogens, vol. 2013, 2013.\nReyahi, 2015, Foxf2 is required for brain pericyte differentiation' and development and maintenance of the blood-brain barrier, Dev Cell, 34, 19, 10.1016\u002Fj.devcel.2015.05.008\nEl Wakil, 2006\nRahman, 2019, Networkbased approach to identify molecular signatures and therapeutic agents in alzheimer's disease, Comput Biol Chem, 78, 431, 10.1016\u002Fj.compbiolchem.2018.12.011\nRahman, 2020, Comprehensive analysis of rna-seq gene expression profiling of brain transcriptomes reveals novel genes, regulators, and pathways in autism spectrum disorder, Brain Sci, 10, 747, 10.3390\u002Fbrainsci10100747\nKnoll, 2009, Functional versatility of transcription factors in the nervous system: the srf paradigm, Trends Neurosci, 32, 432, 10.1016\u002Fj.tins.2009.05.004\nLiou, 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2021, Early diagnosis of aortic aneurysms based on the classification of transfer function parameters estimated from two photoplethysmographic signals, Inform Med Unlocked, 25, 10.1016\u002Fj.imu.2021.100652\nCriqui, 1992, Mortality over a period of 10 years in patients with peripheral arterial disease, N Engl J Med, 326, 381, 10.1056\u002FNEJM199202063260605\nAshton, 2002, The multicentre aneurysm screening study (MASS) into the effect of abdominal aortic aneurysm screening on mortality in men: A randomised controlled trial, Lancet, 360, 1531, 10.1016\u002FS0140-6736(02)11522-4\nNorman, 2004, Population based randomised controlled trial on impact of screening on mortality from abdominal aortic aneurysm, Br Med J, 329, 1259, 10.1136\u002Fbmj.38272.478438.55\nKrüger, 2012, Acute aortic dissection type A, Br J Surg, 99, 1331, 10.1002\u002Fbjs.8840\n2020\nErbel, 2014, Eur Heart J, 35, 2873, 10.1093\u002Feurheartj\u002Fehu281\nConcannon, 2014, Diagnostic accuracy of non-radiologist 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Towards rapid releases in large-scale XaaS development at ericsson: a case study. 2014 IEEE 9th international conference on global software engineering; 2014 18-21 Aug. 2014; shanghai, China IEEE.\nTerzo O, Ruiu P, Bucci E, Xhafa F, editors. Data as a service (DaaS) for sharing and processing of large data collections in the cloud. 2013 seventh international conference on complex, intelligent, and software intensive systems; 2013 3-5 july 2013; taichung, taiwan IEEE.\nGranados Moreno, 2017, Public-private partnerships in cloud-computing services in the context of genomic research, Front Med, 4, 15, 10.3389\u002Ffmed.2017.00003\nLaszewski, 2012, Chapter 1 - migrating to the cloud: client\u002Fserver migrations to the oracle cloud, 1\nMoon, 2019, A heterogeneous IoT data analysis framework with collaboration of edge-cloud computing: focusing on indoor PM10 and PM2.5 status prediction, Sensors, 19, 3038, 10.3390\u002Fs19143038\nXia, 2022, Cancer statistics in China and United States, 2022: profiles, trends, and determinants, Chin Med J (Engl)., 135, 584, 10.1097\u002FCM9.0000000000002108\nTsoi, 2018, Data visualization with IBM watson analytics for global cancer trends comparison from world health organization, Int J Healthc Inf Syst Inf, 13, 45, 10.4018\u002FIJHISI.2018010104\nAbdar, 2020, A new nested ensemble technique for automated diagnosis of breast cancer, Pattern Recogn Lett, 132, 123, 10.1016\u002Fj.patrec.2018.11.004\nAbdar, 2019, CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer, Measurement, 146, 557, 10.1016\u002Fj.measurement.2019.05.022\nSadoughi, 2017, Health information system in a cloud computing context, Stud Health Technol Inf, 236, 290\nSadoughi, 2019, Evaluating the factors that influence cloud technology adoption-comparative case analysis of health and non-health sectors: a systematic review, Health Inf J\nErfannia, 2018, The advantages of implementing cloud computing in the health industry of Iran: a qualitative study, International Journal of Computer Science and Network Security, 18, 198\nYazdani, 2020, Automated misspelling detection and correction in Persian clinical text, J Digit Imag, 33, 555, 10.1007\u002Fs10278-019-00296-y\nDang, 2019, A survey on internet of things and cloud computing for healthcare, Electronics, 8, 768, 10.3390\u002Felectronics8070768\nMubarakali, 2020, Healthcare services monitoring in cloud using secure and robust healthcare-based BLOCKCHAIN(SRHB)approach, Mobile Network Appl, 25, 1330, 10.1007\u002Fs11036-020-01551-1",{"EN":705},"How does cloud computing improve cancer information management? 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cancer, J Digit Imag, 23, 611, 10.1007\u002Fs10278-009-9257-x\nBanik, 2010, Detection of architectural distortion in prior mammograms, IEEE Trans Med Imag, 30, 279, 10.1109\u002FTMI.2010.2076828\nOlaide Nathaniel Oyelade, 2020, A state-of-the-art survey on deep learning methods for detection of architectural distortion from digital mammography, IEEE Access, 8, 148644, 10.1109\u002FACCESS.2020.3016223\nLiu, 2016, A new feature selection method for the detection of architectural distortion in mammographic images, vol. 10033, 1003341\nLiu, 2018, Multiple tbsvm-rfe for the detection of architectural distortion in mammographic images, Multimed Tool Appl, 77, 15773, 10.1007\u002Fs11042-017-5150-7\nZyout, 2018, A computer-aided detection of the architectural distortion in digital mammograms using the fractal dimension measurements of bemd, Comput Med Imag Graph, 70, 173, 10.1016\u002Fj.compmedimag.2018.04.001\nOlawuyi, 2013, Detecting architectural distortion in mammograms using a gabor 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A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images. 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