{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:08:34Z","timestamp":1771650514382,"version":"3.50.1"},"reference-count":175,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T00:00:00Z","timestamp":1552435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["016648 POCI \u2212 01 \u2212 0145 \u2212 FEDER \u2212 016648; COMPETE POCI \u2212 01 \u2212 0145 \u2212 FEDER \u2212 007440; PEst \u2212 OE\/QUI\/UI0313\/2014; POCI \u2212 01 \u2212 0145 \u2212 FEDER \u2212 007630; SFRH\/BD\/95459\/2013; SFRH\/BPD\/71683\/2010"],"award-info":[{"award-number":["016648 POCI \u2212 01 \u2212 0145 \u2212 FEDER \u2212 016648; COMPETE POCI \u2212 01 \u2212 0145 \u2212 FEDER \u2212 007440; PEst \u2212 OE\/QUI\/UI0313\/2014; POCI \u2212 01 \u2212 0145 \u2212 FEDER \u2212 007630; SFRH\/BD\/95459\/2013; SFRH\/BPD\/71683\/2010"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceutics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/pharmaceutics11030119","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T04:15:29Z","timestamp":1552536929000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Computational Approaches in Theranostics: Mining and Predicting Cancer Data"],"prefix":"10.3390","volume":"11","author":[{"given":"T\u00e2nia F. G. G.","family":"Cova","sequence":"first","affiliation":[{"name":"Coimbra Chemistry Centre, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, 3004-535 Coimbra, Portugal"}]},{"given":"Daniel J.","family":"Bento","sequence":"additional","affiliation":[{"name":"Coimbra Chemistry Centre, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, 3004-535 Coimbra, Portugal"}]},{"given":"Sandra C. C.","family":"Nunes","sequence":"additional","affiliation":[{"name":"Coimbra Chemistry Centre, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, 3004-535 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,13]]},"reference":[{"key":"ref_1","first-page":"4","article-title":"From \u201cOne-Size-Fits-All\u201d to a precision medicine approach in neurooncology and neurology practice","volume":"1","author":"Kong","year":"2017","journal-title":"J. Neurol. Clin. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, X., and Wong, S.T. (2014). Cancer theranostics: An introduction. Cancer Theranostics, Elsevier.","DOI":"10.1016\/B978-0-12-407722-5.00001-3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1200\/JOP.18.00154","article-title":"Moving Away From a One-Size-Fits-All Approach to Gastric Cancer","volume":"14","author":"Klute","year":"2018","journal-title":"J. Oncol. Pract."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1158\/0008-5472.CAN-13-0759","article-title":"Bridging population and tissue scale tumor dynamics: A new paradigm for understanding differences in tumor growth and metastatic disease","volume":"74","author":"Gallaher","year":"2014","journal-title":"Cancer Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gde.2014.12.001","article-title":"Dissecting cancer evolution at the macro-heterogeneity and micro-heterogeneity scale","volume":"30","author":"Barber","year":"2015","journal-title":"Curr. Opin. Genet. Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22498","DOI":"10.1038\/srep22498","article-title":"Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates","volume":"6","author":"Sun","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.bbcan.2018.04.009","article-title":"Applications of metabolomics to study cancer metabolism","volume":"1870","author":"Kaushik","year":"2018","journal-title":"Biochim. Biophys. Acta (BBA) Rev. Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ctrv.2018.04.012","article-title":"Metabolomics in breast cancer: A decade in review","volume":"67","author":"McCartney","year":"2018","journal-title":"Cancer Treat. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.aca.2009.11.042","article-title":"Chemometrics in metabolomics\u2014A review in human disease diagnosis","volume":"659","author":"Madsen","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_10","first-page":"1","article-title":"Biological Networks for Cancer Candidate Biomarkers Discovery","volume":"15","author":"Yan","year":"2016","journal-title":"Cancer Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"D595","DOI":"10.1093\/nar\/gkx994","article-title":"TCSBN: A database of tissue and cancer specific biological networks","volume":"46","author":"Lee","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1093\/bfgp\/els045","article-title":"Biological network analysis: Insights into structure and functions","volume":"11","author":"Ma","year":"2012","journal-title":"Brief. Funct. Genom."},{"key":"ref_13","unstructured":"Liu, C. (2017). Computational Integrative Analysis of Biological Networks in Cancer. [Ph.D. Dissertation, University of Helsinki]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17386","DOI":"10.1038\/srep17386","article-title":"Detection of gene communities in multi-networks reveals cancer drivers","volume":"5","author":"Cantini","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1038\/s42003-018-0268-3","article-title":"Representing dynamic biological networks with multi-scale probabilistic models","volume":"2","author":"Kracher","year":"2019","journal-title":"Commun. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e160","DOI":"10.1038\/oncsis.2015.19","article-title":"Atlas of Cancer Signalling Network: A systems biology resource for integrative analysis of cancer data with Google Maps","volume":"4","author":"Kuperstein","year":"2015","journal-title":"Oncogenesis"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.coche.2018.02.005","article-title":"Logical versus kinetic modeling of biological networks: Applications in cancer research","volume":"21","author":"Calzone","year":"2018","journal-title":"Curr. Opin. Chem. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chung, Y.-L., and Griffiths, J. (2008). Using metabolomics to monitor anticancer drugs. Oncogenes Meet Metabolism, Springer.","DOI":"10.1007\/2789_2008_089"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1200\/JCO.2006.09.7550","article-title":"Metabolomics: Available Results, Current Research Projects in Breast Cancer, and Future Applications","volume":"25","author":"Claudino","year":"2007","journal-title":"J. Clin. Oncol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1586\/14789450.4.3.389","article-title":"NMR-based metabolomics approach to target biomarkers for human prostate cancer","volume":"4","author":"Jordan","year":"2007","journal-title":"Expert Rev. Proteom."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2217\/14796694.4.1.93","article-title":"Metabolomics in biomarker discovery: Future uses for cancer prevention","volume":"4","author":"Kim","year":"2008","journal-title":"Future Oncol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2882","DOI":"10.1021\/pr800110a","article-title":"Benign and Atypical Meningioma Metabolic Signatures by High-Resolution Magic-Angle Spinning Molecular Profiling","volume":"7","author":"Morales","year":"2008","journal-title":"J. Proteome Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.jpba.2008.12.010","article-title":"Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm","volume":"49","author":"Tan","year":"2009","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1002\/pros.20727","article-title":"The metabolites citrate, myo-inositol, and spermine are potential age-independent markers of prostate cancer in human expressed prostatic secretions","volume":"68","author":"Serkova","year":"2008","journal-title":"Prostate"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1038\/nature07762","article-title":"Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression","volume":"457","author":"Sreekumar","year":"2009","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4918","DOI":"10.1158\/0008-5472.CAN-08-4806","article-title":"Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis Time-of-Flight Mass Spectrometry","volume":"69","author":"Hirayama","year":"2009","journal-title":"Cancer Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1002\/nbm.1345","article-title":"Metabolite profiling of fecal water extracts from human colorectal cancer","volume":"22","author":"Morales","year":"2009","journal-title":"NMR Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1002\/rcm.3898","article-title":"Development and validation of a gas chromatography\/mass spectrometry method for the metabolic profiling of human colon tissue","volume":"23","author":"Mal","year":"2009","journal-title":"Rapid Commun. Mass Spectrom."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1021\/pr8006232","article-title":"Metabolic Profiling of Human Colorectal Cancer Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) Spectroscopy and Gas Chromatography Mass Spectrometry (GC\/MS)","volume":"8","author":"Chan","year":"2009","journal-title":"J. Proteome Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.aca.2008.06.051","article-title":"Metabonomic profiling of renal cell carcinoma: High-resolution proton nuclear magnetic resonance spectroscopy of human serum with multivariate data analysis","volume":"624","author":"Gao","year":"2008","journal-title":"Anal. Chim. Acta"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1074\/mcp.M800165-MCP200","article-title":"Urine Metabolomics Analysis for Kidney Cancer Detection and Biomarker Discovery","volume":"8","author":"Kim","year":"2009","journal-title":"Mol. Cell. Proteom."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ab.2007.01.028","article-title":"A comprehensive urinary metabolomic approach for identifying kidney cancer","volume":"363","author":"Kind","year":"2007","journal-title":"Anal. Biochem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1111\/j.1349-7006.2009.01086.x","article-title":"Application of 1H NMR-based metabonomics in the study of metabolic profiling of human hepatocellular carcinoma and liver cirrhosis","volume":"100","author":"Gao","year":"2009","journal-title":"Cancer Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jchromb.2004.09.032","article-title":"Diagnosis of liver cancer using HPLC-based metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases","volume":"813","author":"Yang","year":"2004","journal-title":"J. Chromatogr. B"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2422","DOI":"10.1016\/j.juro.2008.01.084","article-title":"Detection of Bladder Cancer in Human Urine by Metabolomic Profiling Using High Performance Liquid Chromatography\/Mass Spectrometry","volume":"179","author":"Issaq","year":"2008","journal-title":"J. Urol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.oraloncology.2007.06.007","article-title":"A metabonomic approach to the diagnosis of oral squamous cell carcinoma, oral lichen planus and oral leukoplakia","volume":"44","author":"Yan","year":"2008","journal-title":"Oral Oncol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1593\/neo.81396","article-title":"Early Stage Diagnosis of Oral Cancer Using 1H NMR\u2013Based Metabolomics","volume":"11","author":"Tiziani","year":"2009","journal-title":"Neoplasia"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40658-018-0216-9","article-title":"Molecular imaging using the theranostic agent 197 (m) Hg: Phantom measurements and Monte Carlo simulations","volume":"5","author":"Freudenberg","year":"2018","journal-title":"EJNMMI Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1002\/jbio.201500344","article-title":"Beam and tissue factors affecting Cherenkov image intensity for quantitative entrance and exit dosimetry on human tissue","volume":"10","author":"Zhang","year":"2017","journal-title":"J. Biophotonics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.7150\/thno.15132","article-title":"Comparison between Three Promising \u00df-emitting Radionuclides, 67Cu, 47Sc and 161Tb, with Emphasis on Doses Delivered to Minimal Residual Disease","volume":"6","author":"Champion","year":"2016","journal-title":"Theranostics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12364","DOI":"10.1021\/jp905323y","article-title":"Monte Carlo Simulations and Atomic Calculations for Auger Processes in Biomedical Nanotheranostics","volume":"113","author":"Montenegro","year":"2009","journal-title":"J. Phys. Chem. A"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.2967\/jnumed.114.153502","article-title":"Monte Carlo evaluation of Auger electron-emitting theranostic radionuclides","volume":"56","author":"Falzone","year":"2015","journal-title":"J. Nucl. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5367","DOI":"10.1002\/mp.12470","article-title":"Sensitivity evaluation and selective plane imaging geometry for x-ray-induced luminescence imaging","volume":"44","author":"Quigley","year":"2017","journal-title":"Med. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/s41598-017-00836-y","article-title":"Magnetic nanoparticles coated with polyarabic acid demonstrate enhanced drug delivery and imaging properties for cancer theranostic applications","volume":"7","author":"Patitsa","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3389\/fchem.2018.00002","article-title":"Structural Insights into the Osteopontin-Aptamer Complex by Molecular Dynamics Simulations","volume":"6","author":"Chelli","year":"2018","journal-title":"Front. Chem."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.7150\/thno.12398","article-title":"Structure-based design of peptides with high affinity and specificity to HER2 positive tumors","volume":"5","author":"Geng","year":"2015","journal-title":"Theranostics"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1016\/j.bbamem.2018.05.021","article-title":"Designing effective anticancer-radiopeptides. A Molecular Dynamics study of their interaction with model tumor and healthy cell membranes","volume":"1860","author":"Capozzi","year":"2018","journal-title":"Biochim. Biophys. Acta (BBA) Biomembr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1080\/17460441.2016.1174688","article-title":"Computational modeling in melanoma for novel drug discovery","volume":"11","author":"Pennisi","year":"2016","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1021\/acs.jproteome.5b01029","article-title":"Approaches to Sample Size Determination for Multivariate Data: Applications to PCA and PLS-DA of Omics Data","volume":"15","author":"Saccenti","year":"2016","journal-title":"J. Proteome Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jconrel.2018.04.042","article-title":"Ignoring the modeling approaches: Towards the shadowy paths in nanomedicine","volume":"280","author":"Hassanzadeh","year":"2018","journal-title":"J. Control. Release"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.flm.2017.06.001","article-title":"Molecular targeted therapy of cancer: The progress and future prospect","volume":"1","author":"Ke","year":"2017","journal-title":"Front. Lab. Med."},{"key":"ref_52","first-page":"1159","article-title":"Big Data, Machine Learning, and Molecular Imaging","volume":"59","author":"Morris","year":"2018","journal-title":"J. Nucl. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1074\/mcp.O116.059253","article-title":"Omics Profiling in Precision Oncology","volume":"15","author":"Yu","year":"2016","journal-title":"Mol. Cell. Proteom. MCP"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jprot.2017.08.010","article-title":"Clinical multi-omics strategies for the effective cancer management","volume":"188","author":"Yoo","year":"2018","journal-title":"J. Proteom."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jbi.2012.09.005","article-title":"Is standard multivariate analysis sufficient in clinical and epidemiological studies?","volume":"46","author":"Cova","year":"2013","journal-title":"J. Biomed. Inform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.etp.2013.09.001","article-title":"Improving discrimination in the grading of rat mammary tumors using two-dimensional mapping of histopathological observations","volume":"66","author":"Lopes","year":"2014","journal-title":"Exp. Toxicol. Pathol."},{"key":"ref_57","unstructured":"Milani, C., and Jadavji, N.M. (2017). Solving cancer: The use of artificial neural networks in cancer diagnosis and treatment. J. Young Investig., 33."},{"key":"ref_58","first-page":"41","article-title":"Applications of Support Vector Machine (SVM) Learning in Cancer Genomics","volume":"15","author":"Huang","year":"2017","journal-title":"Cancer Genom. Proteom."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"20130505","DOI":"10.1098\/rsif.2013.0505","article-title":"Reverse engineering and identification in systems biology: Strategies, perspectives and challenges","volume":"11","year":"2014","journal-title":"J. R. Soc. Interface"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich, F., Kaltenbacher, B., Theis, F.J., and Hasenauer, J. (2017). Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks. PLoS Comput. Biol., 13.","DOI":"10.1371\/journal.pcbi.1005331"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Muhamed Ali, A., Zhuang, H., Ibrahim, A., Rehman, O., Huang, M., and Wu, A. (2018). A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data. Appl. Sci., 8.","DOI":"10.3390\/app8122422"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.procs.2016.04.224","article-title":"Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis","volume":"83","author":"Asri","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, X., and Wong, S. (2014). Chapter 2\u2014Genomics-Based Cancer Theranostics. Cancer Theranostics, Academic Press.","DOI":"10.1016\/B978-0-12-407722-5.00001-3"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"298","DOI":"10.3389\/fphar.2017.00298","article-title":"On the Integration of In Silico Drug Design Methods for Drug Repurposing","volume":"8","author":"Pinzi","year":"2017","journal-title":"Front. Pharmacol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"219","DOI":"10.3389\/fonc.2017.00219","article-title":"Models of Models: A Translational Route for Cancer Treatment and Drug Development","volume":"7","author":"Ogilvie","year":"2017","journal-title":"Front. Oncol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Pennisi, M., Pappalardo, F., Palladini, A., Nicoletti, G., Nanni, P., Lollini, P.-L., and Motta, S. (2010). Modeling the competition between lung metastases and the immune system using agents. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-S7-S13"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3389\/fonc.2018.00240","article-title":"Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning","volume":"8","author":"Izadyyazdanabadi","year":"2018","journal-title":"Front. Oncol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.mrgentox.2012.01.005","article-title":"Prediction in the face of uncertainty: A Monte Carlo-based approach for systems biology of cancer treatment","volume":"746","author":"Wierling","year":"2012","journal-title":"Mutat. Res.\/Genet. Toxicol. Environ. Mutagen."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ddtec.2015.07.002","article-title":"Network and systems biology: Essential steps in virtualising drug discovery and development","volume":"15","author":"Wierling","year":"2015","journal-title":"Drug Discov. Today Technol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"R\u00f6hr, C., Kerick, M., Fischer, A., K\u00fchn, A., Kashofer, K., Timmermann, B., Daskalaki, A., Meinel, T., Drichel, D., and B\u00f6rno, S.T. (2013). High-Throughput miRNA and mRNA Sequencing of Paired Colorectal Normal, Tumor and Metastasis Tissues and Bioinformatic Modeling of miRNA-1 Therapeutic Applications. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0067461"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1002\/biot.201400109","article-title":"Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack","volume":"9","author":"Henderson","year":"2014","journal-title":"Biotechnol. J."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Henriques, D., Villaverde, A.F., Rocha, M., Saez-Rodriguez, J., and Banga, J.R. (2017). Data-driven reverse engineering of signaling pathways using ensembles of dynamic models. PLoS Comput. Biol., 13.","DOI":"10.1371\/journal.pcbi.1005379"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Rao, S., der Schaft, A.V., Eunen, K.V., Bakker, B.M., and Jayawardhana, B. (2014). A model reduction method for biochemical reaction networks. BMC Syst. Biol., 8.","DOI":"10.1186\/1752-0509-8-52"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"878","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. Syst. Biol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1126\/science.1069492","article-title":"Systems Biology: A Brief Overview","volume":"295","author":"Kitano","year":"2002","journal-title":"Science"},{"key":"ref_76","unstructured":"Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., and Herwig, R. (2016). Systems Biology: A Textbook, John Wiley & Sons."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"14262","DOI":"10.1038\/ncomms14262","article-title":"Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors","volume":"8","author":"Risch","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3389\/fonc.2017.00002","article-title":"Patient-derived xenograft models of non-small cell lung cancer and their potential utility in personalized medicine","volume":"7","author":"Morgan","year":"2017","journal-title":"Front. Oncol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.cell.2015.08.068","article-title":"Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges","volume":"163","author":"Day","year":"2015","journal-title":"Cell"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"137","DOI":"10.15252\/emmm.201606857","article-title":"Genetically engineered mouse models in oncology research and cancer medicine","volume":"9","author":"Kersten","year":"2017","journal-title":"EMBO Mol. Med."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.gde.2012.01.004","article-title":"Using genetically engineered mouse models to validate candidate cancer genes and test new therapeutic approaches","volume":"22","author":"Jonkers","year":"2012","journal-title":"Curr. Opin. Genet. Dev."},{"key":"ref_82","first-page":"95","article-title":"Predictive Modeling of Drug Treatment in the Area of Personalized Medicine","volume":"14","author":"Ogilvie","year":"2015","journal-title":"Cancer Inform."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"4221","DOI":"10.2174\/0929867323666160926150617","article-title":"Overview of Systems Biology and Omics Technologies","volume":"23","author":"Karahalil","year":"2016","journal-title":"Curr. Med. Chem."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1576\/toag.13.3.189.27672","article-title":"\u2018Omic\u2019 technologies: Genomics, transcriptomics, proteomics and metabolomics","volume":"13","author":"Horgan","year":"2011","journal-title":"Obstet. Gynaecol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s13059-017-1215-1","article-title":"Multi-omics approaches to disease","volume":"18","author":"Hasin","year":"2017","journal-title":"Genome Biol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ebiom.2018.01.044","article-title":"The New Age of -omics in Urothelial Cancer\u2014Re-wording Its Diagnosis and Treatment","volume":"28","author":"Katsila","year":"2018","journal-title":"EBioMedicine"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Hosen, M., Ahmed, M., and Shekhar, H.U. (2018). Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. BioMed Res. Int., 2018.","DOI":"10.1155\/2018\/9836256"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"14","DOI":"10.5483\/BMBRep.2018.51.1.237","article-title":"Integration of metabolomics and transcriptomics in nanotoxicity studies","volume":"51","author":"Shin","year":"2018","journal-title":"BMB Rep."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.7150\/thno.11543","article-title":"MicroRNAs: New biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer","volume":"5","author":"Bertoli","year":"2015","journal-title":"Theranostics"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Devlin, K.L., Sanford, T., Harrison, L.M., LeBourgeois, P., Lashinger, L.M., Mambo, E., and Hursting, S.D. (2016). Stage-Specific MicroRNAs and Their Role in the Anticancer Effects of Calorie Restriction in a Rat Model of ER-Positive Luminal Breast Cancer. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0159686"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1373\/clinchem.2015.242800","article-title":"MicroRNA Theranostics in Prostate Cancer Precision Medicine","volume":"62","author":"Matin","year":"2016","journal-title":"Clin. Chem."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Cava, C., Bertoli, G., Colaprico, A., Bontempi, G., Mauri, G., and Castiglioni, I. (2018). In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer. Int. J. Mol. Sci., 19.","DOI":"10.3390\/ijms19030910"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.7150\/thno.18262","article-title":"Cancer-derived circulating MicroRNAs promote tumor angiogenesis by entering dendritic cells to degrade highly complementary MicroRNAs","volume":"7","author":"Wang","year":"2017","journal-title":"Theranostics"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"7750","DOI":"10.1073\/pnas.1605841113","article-title":"Theranostic near-infrared fluorescent nanoplatform for imaging and systemic siRNA delivery to metastatic anaplastic thyroid cancer","volume":"113","author":"Liu","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4106","DOI":"10.1002\/smll.201400963","article-title":"Combined Magnetic Nanoparticle-based MicroRNA and Hyperthermia Therapy to Enhance Apoptosis in Brain Cancer Cells","volume":"10","author":"Yin","year":"2014","journal-title":"Small"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"S34","DOI":"10.1080\/00365513.2016.1208444","article-title":"The potential of miRNAs for diagnosis, treatment and monitoring of breast cancer","volume":"76","author":"Bertoli","year":"2016","journal-title":"Scand. J. Clin. Lab. Investig."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Bertoli, G., Cava, C., and Castiglioni, I. (2016). MicroRNAs as Biomarkers for Diagnosis, Prognosis and Theranostics in Prostate Cancer. Int. J. Mol. Sci., 17.","DOI":"10.3390\/ijms17030421"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.2741\/4571","article-title":"A review of computational approaches detecting microRNAs involved in cancer","volume":"22","author":"Cantini","year":"2017","journal-title":"Front. Biosci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1038\/ng0407-426","article-title":"PharmGKB: A logical home for knowledge relating genotype to drug response phenotype","volume":"39","author":"Altman","year":"2007","journal-title":"Nat. Genet."},{"key":"ref_100","first-page":"109","article-title":"Immune system modelling by top-down and bottom-up approaches","volume":"7","author":"Bianca","year":"2012","journal-title":"Int. Math. Forum"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1146\/annurev-pharmtox-010611-134630","article-title":"Novel computational approaches to polypharmacology as a means to define responses to individual drugs","volume":"52","author":"Xie","year":"2012","journal-title":"Ann. Rev. Pharmacol. Toxicol."},{"key":"ref_102","first-page":"3131","article-title":"Insight into molecular dynamics simulation of BRAF (V600E) and potent novel inhibitors for malignant melanoma","volume":"10","author":"Tang","year":"2015","journal-title":"Int. J. Nanomed."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1088\/1478-3967\/1\/3\/006","article-title":"The statistical mechanics of complex signaling networks: Nerve growth factor signaling","volume":"1","author":"Brown","year":"2004","journal-title":"Phys. Biol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13321-015-0055-9","article-title":"Systems biology approaches for advancing the discovery of effective drug combinations","volume":"7","author":"Ryall","year":"2015","journal-title":"J. Cheminform."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1016\/j.ces.2009.01.041","article-title":"Fuzzy modeling of signal transduction networks","volume":"64","author":"Huang","year":"2009","journal-title":"Chem. Eng. Sci."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Reis, Y., Bernardo-Faura, M., Richter, D., Wolf, T., Brors, B., Hamacher-Brady, A., Eils, R., and Brady, N.R. (2012). Multi-Parametric Analysis and Modeling of Relationships between Mitochondrial Morphology and Apoptosis. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0028694"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Azad, A., Lawen, A., and Keith, J.M. (2015). Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling. BMC Syst. Biol., 9.","DOI":"10.1186\/s12918-014-0135-x"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1158\/0008-5472.CAN-16-0097","article-title":"Quantification of pathway crosstalk reveals novel synergistic drug combinations for breast cancer","volume":"77","author":"Jaeger","year":"2017","journal-title":"Cancer Res."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1186\/s12967-018-1535-2","article-title":"In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition","volume":"16","author":"Cava","year":"2018","journal-title":"J. Transl. Med."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"63995","DOI":"10.18632\/oncotarget.11745","article-title":"Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination","volume":"7","author":"Sun","year":"2016","journal-title":"Oncotarget"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/1742-4682-10-41","article-title":"Multi-scale agent-based modeling on melanoma and its related angiogenesis analysis","volume":"10","author":"Wang","year":"2013","journal-title":"Theoret. Biol. Med. Model."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1146\/annurev-bioeng-071910-124729","article-title":"Multiscale Cancer Modeling","volume":"13","author":"Deisboeck","year":"2011","journal-title":"Ann. Rev. Biomed. Eng."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.7150\/thno.23767","article-title":"Integrative analysis of imaging and transcriptomic data of the immune landscape associated with tumor metabolism in lung adenocarcinoma: Clinical and prognostic implications","volume":"8","author":"Choi","year":"2018","journal-title":"Theranostics"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3526","DOI":"10.1118\/1.2241995","article-title":"Delay differential equations and the dose-time dependence of early radiotherapy reactions","volume":"33","author":"Fenwick","year":"2006","journal-title":"Med. Phys."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1093\/bioinformatics\/bti426","article-title":"Modeling and simulation of cancer immunoprevention vaccine","volume":"21","author":"Pappalardo","year":"2005","journal-title":"Bioinformatics"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Pappalardo, F., Forero, I.M., Pennisi, M., Palazon, A., Melero, I., and Motta, S. (2011). SimB16: Modeling Induced Immune System Response against B16-Melanoma. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0026523"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1080\/14712598.2016.1223622","article-title":"Employing dynamical computational models for personalizing cancer immunotherapy","volume":"16","author":"Agur","year":"2016","journal-title":"Expert Opin. Biol. Ther."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1016\/S0895-7177(03)00125-0","article-title":"Bifurcation analysis for a mean field modelling of tumor and immune system competition","volume":"37","author":"Marasco","year":"2003","journal-title":"Math. Comput. Model."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s002850050127","article-title":"Modeling immunotherapy of the tumor\u2013immune interaction","volume":"37","author":"Kirschner","year":"1998","journal-title":"J. Math. Biol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/BF02460644","article-title":"Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis","volume":"56","author":"Kuznetsov","year":"1994","journal-title":"Bull. Math. Biol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"389","DOI":"10.2478\/v10006-008-0035-6","article-title":"Immunotherapy with interleukin-2: A study based on mathematical modeling","volume":"18","author":"Banerjee","year":"2008","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/0959-8049(94)90122-8","article-title":"Immunotherapy of metastatic melanoma with interferon-\u03b1 and interleukin-2: Pattern of progression in responders and patients with stable disease with or without resection of residual lesions","volume":"30","author":"Keilholz","year":"1994","journal-title":"Eur. J. Cancer"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1200\/JCO.1996.14.6.1778","article-title":"Prediction of response to treatment in superficial bladder carcinoma through pattern of interleukin-2 gene expression","volume":"14","author":"Kaempfer","year":"1996","journal-title":"J. Clin. Oncol."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jtbi.2005.06.037","article-title":"Mixed immunotherapy and chemotherapy of tumors: Modeling, applications and biological interpretations","volume":"238","author":"Gu","year":"2006","journal-title":"J. Theor. Biol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jtbi.2011.02.008","article-title":"A mathematical model of combined bacillus Calmette-Guerin (BCG) and interleukin (IL)-2 immunotherapy of superficial bladder cancer","volume":"277","author":"Gluckman","year":"2011","journal-title":"J. Theor. Biol."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"7293","DOI":"10.1158\/0008-5472.CAN-06-0241","article-title":"Cancer immunotherapy by interleukin-21: Potential treatment strategies evaluated in a mathematical model","volume":"66","author":"Cappuccio","year":"2006","journal-title":"Cancer Res."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Gallagher, S.R., Coon, W., Donley, K., Scott, A., and Goldberg, D.S. (2011). A First Attempt to Bring Computational Biology into Advanced High School Biology Classrooms. PLoS Comput. Biol., 7.","DOI":"10.1371\/journal.pcbi.1002244"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Elishmereni, M., Kheifetz, Y., S\u00f8ndergaard, H., Overgaard, R.V., and Agur, Z. (2011). An Integrated Disease\/Pharmacokinetic\/Pharmacodynamic Model Suggests Improved Interleukin-21 Regimens Validated Prospectively for Mouse Solid Cancers. PLoS Comput. Biol., 7.","DOI":"10.1371\/journal.pcbi.1002206"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.jtbi.2007.04.003","article-title":"Cancer immunotherapy, mathematical modeling and optimal control","volume":"247","author":"Castiglione","year":"2007","journal-title":"J. Theor. Biol."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Kronik, N., Kogan, Y., Elishmereni, M., Halevi-Tobias, K., Vuk-Pavlovi\u0107, S., and Agur, Z. (2010). Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0015482"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1158\/0008-5472.CAN-11-4166","article-title":"Reconsidering the paradigm of cancer immunotherapy by computationally aided real-time personalization","volume":"72","author":"Kogan","year":"2012","journal-title":"Cancer Res."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"4469","DOI":"10.1158\/1078-0432.CCR-04-2337","article-title":"Delayed disease progression after allogeneic cell vaccination in hormone-resistant prostate cancer and correlation with immunologic variables","volume":"11","author":"Michael","year":"2005","journal-title":"Clin. Cancer Res."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"170","DOI":"10.3389\/fphys.2018.00170","article-title":"Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy","volume":"9","author":"Qutub","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s00262-007-0387-z","article-title":"Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics","volume":"57","author":"Kronik","year":"2008","journal-title":"Cancer Immunol. Immunother."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/s41586-018-0830-7","article-title":"De novo design of potent and selective mimics of IL-2 and IL-15","volume":"565","author":"Silva","year":"2019","journal-title":"Nature"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"020058","DOI":"10.1063\/1.5001637","article-title":"Molecular level in silico studies for oncology. Direct models review","volume":"1882","author":"Psakhie","year":"2017","journal-title":"AIP Conf. Proc."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.lfs.2017.06.001","article-title":"Application of modelling and nanotechnology-based approaches: The emergence of breakthroughs in theranostics of central nervous system disorders","volume":"182","author":"Hassanzadeh","year":"2017","journal-title":"Life Sci."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1039\/c2ib20166f","article-title":"Surface modification of the TiO2 nanoparticle surface enables fluorescence monitoring of aggregation and enhanced photoreactivity","volume":"5","author":"Kamps","year":"2013","journal-title":"Integr. Biol."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"3869","DOI":"10.1021\/acs.biomac.7b00810","article-title":"Doxorubicin-Loaded Unimolecular Micelle-Stabilized Gold Nanoparticles as a Theranostic Nanoplatform for Tumor-Targeted Chemotherapy and Computed Tomography Imaging","volume":"18","author":"Lin","year":"2017","journal-title":"Biomacromolecules"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.2217\/nnm.13.117","article-title":"Computational nanomedicine: Modeling of nanoparticle-mediated hyperthermal cancer therapy","volume":"8","author":"Kaddi","year":"2013","journal-title":"Nanomedicine"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.jtbi.2012.11.031","article-title":"The effect of interstitial pressure on tumor growth: Coupling with the blood and lymphatic vascular systems","volume":"320","author":"Wu","year":"2013","journal-title":"J. Theor. Biol."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Frieboes, H.B., Wu, M., Lowengrub, J., Decuzzi, P., and Cristini, V. (2013). A Computational Model for Predicting Nanoparticle Accumulation in Tumor Vasculature. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0056876"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Curtis, L.T., Wu, M., Lowengrub, J., Decuzzi, P., and Frieboes, H.B. (2015). Computational Modeling of Tumor Response to Drug Release from Vasculature-Bound Nanoparticles. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0144888"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s10439-008-9631-8","article-title":"Computational Modeling and Real-Time Control of Patient-Specific Laser Treatment of Cancer","volume":"37","author":"Fuentes","year":"2009","journal-title":"Ann. Biomed. Eng."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1158\/1535-7163.MCT-09-0195","article-title":"A modeling analysis of the effects of molecular size and binding affinity on tumor targeting","volume":"8","author":"Schmidt","year":"2009","journal-title":"Mol. Cancer Thera."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s40846-018-0386-x","article-title":"Computational Design of an RF Controlled Theranostic Model for Evaluation of Tissue Biothermal Response","volume":"38","author":"Awojoyogbe","year":"2018","journal-title":"J. Med. Biol. Eng."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1007\/s11095-017-2245-9","article-title":"Development of Halofluorochromic Polymer Nanoassemblies for the Potential Detection of Liver Metastatic Colorectal Cancer Tumors Using Experimental and Computational Approaches","volume":"34","author":"Reichel","year":"2017","journal-title":"Pharm. Res."},{"key":"ref_148","first-page":"417","article-title":"Rdf Sketch Maps\u2014Knowledge complexity reduction for precision medicine analytics","volume":"21","author":"Thanintorn","year":"2016","journal-title":"Biocomputing"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.jbi.2014.08.003","article-title":"Uncovering influence links in molecular knowledge networks to streamline personalized medicine","volume":"52","author":"Shin","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Shin, D., Arthur, G., Caldwell, C., Popescu, M., Petruc, M., Diaz-Arias, A., and Shyu, C.-R. (2012). A pathologist-in-the-loop IHC antibody test selection using the entropy-based probabilistic method. J. Pathol. Inform., 3.","DOI":"10.4103\/2153-3539.93393"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"e300","DOI":"10.1200\/JCO.2012.45.9495","article-title":"Continued response off treatment after BRAF inhibition in refractory hairy cell leukemia","volume":"31","author":"Dietrich","year":"2013","journal-title":"J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/bjh.12201","article-title":"Rapid response of biallelic BRAF V 600 E mutated hairy cell leukaemia to low dose vemurafenib","volume":"161","author":"Follows","year":"2013","journal-title":"Br. J. Haematol."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.3324\/haematol.2012.082404","article-title":"Low-dose vemurafenib induces complete remission in a case of hairy-cell leukemia with a V600E mutation","volume":"98","author":"Peyrade","year":"2013","journal-title":"Haematologica"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"027106","DOI":"10.1088\/1752-7155\/9\/2\/027106","article-title":"Application of an artificial neural network model for selection of potential lung cancer biomarkers","volume":"9","author":"Tomasz","year":"2015","journal-title":"J. Breath Res."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"45938","DOI":"10.1038\/srep45938","article-title":"Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer","volume":"7","author":"Vandenberghe","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1259\/bjr.75.895.750603","article-title":"CT\u2013MRI image fusion for delineation of volumes in three-dimensional conformal radiation therapy in the treatment of localized prostate cancer","volume":"75","author":"Sannazzari","year":"2002","journal-title":"Br. J. Radiol."},{"key":"ref_157","unstructured":"Puri, M., Pathak, Y., Sutariya, V.K., Tipparaju, S., and Moreno, W. (2016). Chapter 14\u2014ANN in Pharmaceutical Product and Process Development. Artificial Neural Network for Drug Design, Delivery and Disposition, Academic Press."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1021\/ar200106e","article-title":"Molecular Imaging with Theranostic Nanoparticles","volume":"44","author":"Jokerst","year":"2011","journal-title":"Acc. Chem. Res."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"167","DOI":"10.2217\/nnm-2016-0376","article-title":"Improving cancer imaging with magnetic nanoparticles: Where are we now?","volume":"12","author":"Tietze","year":"2017","journal-title":"Nanomedicine"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1038\/nrclinonc.2014.134","article-title":"Quantitative multimodality imaging in cancer research and therapy","volume":"11","author":"Yankeelov","year":"2014","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.jconrel.2010.03.005","article-title":"In vivo MRI multicontrast kinetic analysis of the uptake and intracellular trafficking of paramagnetically labeled liposomes","volume":"144","author":"Terreno","year":"2010","journal-title":"J. Control. Release"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/J.ENG.2016.01.027","article-title":"Tumor Molecular Imaging with Nanoparticles","volume":"2","author":"Cheng","year":"2016","journal-title":"Engineering"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.molonc.2012.02.005","article-title":"Molecular imaging for personalized cancer care","volume":"6","author":"Kircher","year":"2012","journal-title":"Mol. Oncol."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0360-3016(99)00183-2","article-title":"Clinical dose\u2013volume histogram analysis for pneumonitis after 3D treatment for non-small cell lung cancer (NSCLC)","volume":"45","author":"Graham","year":"1999","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1088\/0031-9155\/42\/1\/008","article-title":"Adaptive radiation therapy","volume":"42","author":"Yan","year":"1997","journal-title":"Phys. Med. Biol."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1200\/JCO.2006.09.9515","article-title":"Advances in image-guided radiation therapy","volume":"25","author":"Dawson","year":"2007","journal-title":"J. Clin. Oncol."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1097\/CCO.0000000000000440","article-title":"Recent advances in radiation oncology: Multimodal targeting of high risk and recurrent prostate cancer","volume":"30","author":"Kwok","year":"2018","journal-title":"Curr. Opin. Oncol."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.ijrobp.2018.01.047","article-title":"How advances in imaging will affect precision radiation oncology","volume":"101","author":"Jaffray","year":"2018","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2015","journal-title":"Radiology"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1148\/radiol.2017171503","article-title":"2016 New Horizons Lecture: Beyond Imaging\u2014Radiology of Tomorrow","volume":"286","author":"Hricak","year":"2018","journal-title":"Radiology"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"E6265","DOI":"10.1073\/pnas.1505935112","article-title":"Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images","volume":"112","author":"Fehr","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.1007\/s00330-017-4779-y","article-title":"A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome","volume":"27","author":"Vargas","year":"2017","journal-title":"Eur. Radiol."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"443","DOI":"10.7150\/thno.11107","article-title":"CFD modeling and image analysis of exhaled aerosols due to a growing bronchial tumor: Towards non-invasive diagnosis and treatment of respiratory obstructive diseases","volume":"5","author":"Xi","year":"2015","journal-title":"Theranostics"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"11390","DOI":"10.1038\/s41598-018-29675-1","article-title":"A Spectral Fiedler Field-based Contrast Platform for Imaging of Nanoparticles in Colon Tumor","volume":"8","author":"Liu","year":"2018","journal-title":"Sci. Rep."}],"container-title":["Pharmaceutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4923\/11\/3\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:38:34Z","timestamp":1760186314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4923\/11\/3\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,13]]},"references-count":175,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["pharmaceutics11030119"],"URL":"https:\/\/doi.org\/10.3390\/pharmaceutics11030119","relation":{},"ISSN":["1999-4923"],"issn-type":[{"value":"1999-4923","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,13]]}}}