An Architecture for Integrating Genetic and Clinical Data

Abstract Personalized medicine is the new horizon of the medical science. Its main goal is to improve the quality of patient care, both in prevention and in therapeutic stage, and to improve the precision of therapy through the integrated analysis of clinical, biological and molecular data. Data integration represents a powerful instrument for clinicians to have an overall vision of diseases. Even if clinical data integration has been treated in many recent papers, few results have been presented with respect to integrating proteomics and genomics data. We present the architecture for the integration of genetic and phenotype data extracted from medical records. The focus is information extraction and data prefiling for early detection of chronic diseases. Focus is about cancer diseases where omics data, environmental, ontologies and clinical data can be integrated to improve knowledge about the risk assessment and genetic susceptibility.

[1]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[2]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[3]  Abbas Toloie Eshlaghy,et al.  Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence , 2013 .

[4]  M. West,et al.  Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  M. Steinbach,et al.  Integration of Clinical and Genomic data : a Methodological Survey , 2013 .

[6]  Yves A. Lussier,et al.  An integrative model for in-silico clinical-genomics discovery science , 2002, AMIA.

[7]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[8]  Marcel J. T. Reinders,et al.  Integration of Clinical and Gene Expression Data Has a Synergetic Effect on Predicting Breast Cancer Outcome , 2012, PloS one.

[9]  Mario Cannataro,et al.  SIGMCC: A system for sharing meta patient records in a Peer-to-Peer environment , 2008, Future Gener. Comput. Syst..

[10]  Vincent Ferretti,et al.  Clinical genomics information management software linking cancer genome sequence and clinical decisions. , 2013, Genomics.

[11]  James J. Cimino,et al.  The National Institutes of Health's Biomedical Translational Research Information System (BTRIS): Design, contents, functionality and experience to date , 2014, J. Biomed. Informatics.

[12]  Joaquim Cezar Felipe,et al.  Computational framework to support integration of biomolecular and clinical data within a translational approach , 2013, BMC Bioinformatics.

[13]  Subha Madhavan,et al.  Rembrandt: Helping Personalized Medicine Become a Reality through Integrative Translational Research , 2009, Molecular Cancer Research.

[14]  José Antonio Gómez-Ruiz,et al.  A combined neural network and decision trees model for prognosis of breast cancer relapse , 2003, Artif. Intell. Medicine.

[15]  J. Listgarten,et al.  Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms , 2004, Clinical Cancer Research.

[16]  Olivier Bodenreider,et al.  Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature , 2011, Bioinform..

[17]  Di Zhao,et al.  Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction , 2011, J. Biomed. Informatics.

[18]  Min Zhu,et al.  Ontology driven integration platform for clinical and translational research , 2009, BMC Bioinformatics.

[19]  J. Shavlik,et al.  Breast cancer risk estimation with artificial neural networks revisited , 2010, Cancer.

[20]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[21]  M. Gail,et al.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. , 1989, Journal of the National Cancer Institute.

[22]  Holly Dressman,et al.  Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. , 2003, Human molecular genetics.