Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/107212
Title: Computational Approaches in Theranostics: Mining and Predicting Cancer Data
Authors: Cova, Tânia F. G. G. 
Bento, Daniel J. 
Nunes, Sandra Cristina da Cruz 
Keywords: cancer; theranostics; nanotherapeutics; imaging; in silico models; modeling; simulation
Issue Date: 13-Mar-2019
Publisher: MDPI
Project: 016648 POCI-01-0145-FEDER-016648 
POCI-01-0145-FEDER-007440 
PEst-OE/QUI/UI0313/2014 
POCI-01-0145-FEDER-007630 
SFRH/BD/95459/2013 
SFRH/BPD/71683/2010 
Serial title, monograph or event: Pharmaceutics
Volume: 11
Issue: 3
Abstract: The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.
URI: https://hdl.handle.net/10316/107212
ISSN: 1999-4923
DOI: 10.3390/pharmaceutics11030119
Rights: openAccess
Appears in Collections:I&D CQC - Artigos em Revistas Internacionais

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