Εξατομικευμένη Ιατρική και μαζικά βιοϊατρικά δεδομένα
Abstract
Η εξατομικευμένη ιατρική αναπτύχθηκε τα τελευταία χρόνια ως μια νέα θεραπευτική προσέγγιση που αφορά τη θεραπεία και περίθαλψη ενός ασθενή με γνώμονα τις βιολογικές και περιβαλλοντικές πληροφορίες που έχουν συγκεντρωθεί για αυτόν. Κάθε μέρα ένας μεγάλος όγκος βιοϊατρικών δεδομένων συγκεντρώνεται και αποθηκεύεται σε ηλεκτρονικούς φακέλους υγείας, δημιουργώντας έναν τεράστιο όγκο δεδομένων, τα οποία παρουσιάζουν διάφορες προκλήσεις για τη διαχείριση και την ανάλυσή τους. Διάφοροι τομείς γνώσεων είναι απαραίτητοι για την πρόσκτηση των δεξιοτήτων και ικανοτήτων που απαιτούνται για να αξιοποιούνται πλήρως οι δυνατότητες που προσφέρει αυτός ο μεγάλος όγκος των βιοϊατρικών δεδομένων. Αρχίζουμε με μια εισαγωγή στην έννοια «μαζικά δεδομένα» καθώς και στην αποθήκευση και στη διαχείριση τους. Eξετάζουμε τις στατιστικές μεθόδους και την επιστήμη των δεδομένων, που αποτελούν σημαντικά θεμέλια για την τεχνητή νοημοσύνη, τη μηχανική εκμάθηση και την επεξεργασία φυσικής γλώσσας, απαραίτητες προϋποθέσεις για την ανάπτυξη προγνωστικών μοντέλων με σκοπό τη λήψη κλινικών αποφάσεων. Εν κατακλείδι, προτείνουμε ειδική κατάρτιση για την προετοιμασία της νέας γενιάς επιστημόνων για τον χειρισμό των μαζικών βιοϊατρικών δεδομένων.
References
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8. Phillips HS, Kharbanda S, Chen R, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell. 9(3), 157–173 (2006).
9. Warner JL, Denny JC, Kreda DA, Alterovitz G. Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization. J Am Med Inform Assoc. 22(2), 324–329 (2015).
10. Huang X, Jennings SF, Bruce B, et al. Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm. BioData Min. 8, 7 (2015).
11. Angles R, Gutierrez C. Survey of Graph Database Models. ACM Comput. Surv. 40(1), 1:1–1:39 (2008).
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14. Mamun A-A, Aseltine R, Rajasekaran S. Efficient Record Linkage Algorithms Using Complete Linkage Clustering. PLoS ONE. 11(4), e0154446 (2016).
15. Kim D, Joung J-G, Sohn K-A, et al. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J Am Med Inform Assoc. 22(1), 109–120 (2015).
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20. Huber W, Carey VJ, Gentleman R, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods. 12(2), 115–121 (2015).
21. Peng RD. Reproducible research in computational science. Science. 334(6060), 1226–1227 (2011).
22. Wickham H, Grolemund G. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1 edition. O’Reilly Media, Sebastopol, CA.
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39. Olson RS, Sipper M, Cava WL, et al. A System for Accessible Artificial Intelligence. In: Genetic Programming Theory and Practice XV. Banzhaf W, Olson RS, Tozier W, Riolo R (Eds.). . Springer International Publishing, 121–134 (2018).
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42. Yeh A, Morgan A, Colosimo M, Hirschman L. BioCreAtIvE task 1A: gene mention finding evaluation. BMC Bioinformatics. 6 Suppl 1, S2 (2005).
43. Leaman R, Gonzalez G. BANNER: an executable survey of advances in biomedical named entity recognition. Pac Symp Biocomput. , 652–663 (2008).
44. Garten Y, Altman RB. Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text. BMC Bioinformatics. 10 Suppl 2, S6 (2009).
45. Yang H-T, Ju J-H, Wong Y-T, Shmulevich I, Chiang J-H. Literature-based discovery of new candidates for drug repurposing. Brief. Bioinformatics. 18(3), 488–497 (2017).
46. Crim J, McDonald R, Pereira F. Automatically annotating documents with normalized gene lists. BMC Bioinformatics. 6 Suppl 1, S13 (2005).
47. Chen L, Liu H, Friedman C. Gene name ambiguity of eukaryotic nomenclatures. Bioinformatics. 21(2), 248–256 (2005).
48. Miwa M, Thompson P, McNaught J, Kell DB, Ananiadou S. Extracting semantically enriched events from biomedical literature. BMC Bioinformatics. 13, 108 (2012).
49. Velupillai S, Mowery D, South BR, Kvist M, Dalianis H. Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis. Yearb Med Inform. 10(1), 183–193 (2015).
50. Wolfe BA, Mamlin BW, Biondich PG, et al. The OpenMRS system: collaborating toward an open source EMR for developing countries. AMIA Annu Symp Proc. , 1146 (2006).
51. Huang X, Bruce B, Buchan A, et al. No-boundary thinking in bioinformatics research. BioData Min. 6(1), 19 (2013).
