scholarly journals The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

2007 ◽  
Vol 25 (11) ◽  
pp. 1251-1255 ◽  
Author(s):  
Barry Smith ◽  
◽  
Michael Ashburner ◽  
Cornelius Rosse ◽  
Jonathan Bard ◽  
...  
Keyword(s):  
Data Integration ◽  
Biomedical Data

scholarly journals Διάγνωση, πρόγνωση και υποστήριξη θεραπευτικής αγωγής κακοήθων λεμφωμάτων με χρήση τεχνητής νοημοσύνης

2009 ◽  
Author(s):  
Ιωάννης Δράκος
Keyword(s):  
Data Integration ◽  
Biomedical Data

Η παρούσα διδακτορική διατριβή έχει ως στόχο τη δημιουργία ενός αποδοτικού μοντέλου για το Λειτουργικό Συνδυασμό Βιο-Ιατρικών δεδομένων (BioMedical data integration). Ξεκινώντας από τη σχεδιαστική ανάλυση της ιατρικής γνώσης και των προβλημάτων που προκύπτουν από τον τρόπο παραγωγής των ιατρικών δεδομένων, προχωρεί στην επίλυση των επιμέρους θεμάτων Λειτουργικού Συνδυασμού εντός ενός συγκεκριμένου ιατρικού πεδίου και καταλήγει στον ολοκληρωμένο Λειτουργικό Συνδυασμό ιατρικών δεδομένων προερχόμενων από διαφορετικές πηγές και πεδία γνώσης. Συνεχίζει με τη σχεδίαση ενός μοντέλου βάσεων δεδομένων που ακολουθεί «οριζόντια» λογική και είναι αρκετά αποδοτικό ώστε να αποκρίνεται σε πολύπλοκα και ευρείας κλίμακας ερωτήματα σε πραγματικό χρόνο. Καταλήγει με την παρουσίαση μίας ολοκληρωμένης εφαρμογής η οποία εκμεταλλευόμενη τα πλεονεκτήματα του Λειτουργικού Συνδυασμού και της οριζόντιας δομής των δεδομένων είναι σε θέση να διαχειριστεί εξετάσεις προερχόμενες από κάθε κυτταρομετρητή ροής και συνδυάζοντάς αυτές με τις υπόλοιπες αιματολογικές κλινικοεργαστηριακές εξετάσεις να απαντά σε καθημερινά και σύνθετα ερευνητικά, ιατρικά ερωτήματα. Τα πρωτότυπα ερευνητικά αποτελέσματα που προέκυψαν στα πλαίσια της παρούσης εργασίας δημοσιεύτηκαν σε έγκυρα διεθνή περιοδικά και σε διεθνή και ελληνικά συνέδρια με κριτές.

scholarly journals Translational integrity and continuity: Personalized biomedical data integration

2009 ◽  
Vol 42 (1) ◽  
pp. 100-112 ◽  
Author(s):  
Xiaoming Wang ◽  
Lili Liu ◽  
James Fackenthal ◽  
Shelly Cummings ◽  
Oluwatobi I. Olopade ◽  
...  
Keyword(s):  
Data Integration ◽  
Biomedical Data

scholarly journals A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

2019 ◽  
Vol 171 (1-4) ◽  
pp. 345-365 ◽  
Author(s):  
Leonardo Rundo ◽  
Carmelo Militello ◽  
Salvatore Vitabile ◽  
Giorgio Russo ◽  
Evis Sala ◽  
...  
Keyword(s):  
Image Analysis ◽  
Data Integration ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Biomedical Data

scholarly journals Biomedical Data Integration, Modeling, and Simulation in the Era of Big Data and Translational Medicine

2014 ◽  
Vol 2014 ◽  
pp. 1-1 ◽  
Author(s):  
Bairong Shen ◽  
Andrew E. Teschendorff ◽  
Degui Zhi ◽  
Junfeng Xia
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Modeling And Simulation ◽  
Translational Medicine ◽  
Biomedical Data

scholarly journals DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 54 ◽  
Author(s):  
Priya Deshpande ◽  
Alexander Rasin ◽  
Jacob Furst ◽  
Daniela Raicu ◽  
Sameer Antani
Keyword(s):  
Data Integration ◽  
Biomedical Research ◽  
Research Work ◽  
Data Access ◽  
Global Scale ◽  
Data Driven ◽  
Biomedical Data ◽  
Integration Model ◽  
Healthcare Data ◽  
Data Integration System

Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.

scholarly journals Biomedical Data Integration in Computational Drug Design and Bioinformatics

2013 ◽  
Vol 9 (1) ◽  
pp. 108-117 ◽  
Author(s):  
Jose A. Seoane ◽  
Vanessa Aguiar-Pulido ◽  
Cristian R. Munteanu ◽  
Daniel Rivero ◽  
Juan R. Rabunal ◽  
...  
Keyword(s):  
Drug Design ◽  
Data Integration ◽  
Biomedical Data ◽  
Computational Drug Design

scholarly journals Integrating Heterogeneous Biomedical Data for Cancer Research: the CARPEM infrastructure

2016 ◽  
Vol 07 (02) ◽  
pp. 260-274 ◽  
Author(s):  
Vincent Canuel ◽  
Hector Countouris ◽  
Pierre Laurent-Puig ◽  
Anita Burgun ◽  
Bastien Rance
Keyword(s):  
Cancer Research ◽  
Data Integration ◽  
Translational Research ◽  
Data Access ◽  
Biomedical Data ◽  
Research Platform ◽  
Heterogeneous Nature ◽  
Three Layer Architecture ◽  
Technical Principles ◽  
Integration Problems

SummaryCancer research involves numerous disciplines. The multiplicity of data sources and their heterogeneous nature render the integration and the exploration of the data more and more complex. Translational research platforms are a promising way to assist scientists in these tasks. In this article, we identify a set of scientific and technical principles needed to build a translational research platform compatible with ethical requirements, data protection and data-integration problems. We describe the solution adopted by the CARPEM cancer research program to design and deploy a platform able to integrate retrospective, prospective, and day-to-day care data. We designed a three-layer architecture composed of a data collection layer, a data integration layer and a data access layer. We leverage a set of open-source resources including i2b2 and tranSMART.Citation: Rance B, Canuel V, Countouris H, Laurent-Puig P, Burgun A. Integrating heterogeneous biomedical data for cancer research: the CARPEM infrastructure.

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