HCM

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The main objective of the present work consists in the integration and exploration of clinical and biological data, with the purpose of developing a patient characterization and diagnostic system.
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The objective of the HCM project consists in the integration and exploration of clinical and biological data, with the purpose of developing a patient characterization and prognostic system for the disease Hypertrophic cardiomyopathy (HCM).
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Hypertrophic cardiomyopathy (HCM) is a complex disease, that manifests itself under variable symptoms and to which is associated a great number of genetic mutations. These aspects hinder the obtention of a clinical diagnostic previous to the development of serious symptoms, or even death, due to the difficulty associated with the identification of correlations between the genotypic and phenotypic characteristics of the patients.
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HCM is a relatively common genetic myocardial disorder and the most frequent cause of sudden cardiac death in young people and athletes. It is characterized by a variable clinical presentation and onset, as well as a genetic heterogeneity denoted by 640 known mutations in more than 20 genes. Although the existence of a single mutation is sufficient for a positive diagnosis, the severity of HCM may not be the same for two individuals, even if direct relatives, since the presence of a given mutation can have a benign pattern in one individual and result in sudden cardiac death in another.
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Through the integration of the data obtained during the study of the disease, namely genetic expression, genotyping and the patient clinical history, it will be possible to mine this data in order to identify the implicit correlations that will provide the obtention of a timely diagnostic.
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Given the disease characteristics just referred, the identification of correlations between genotype and phenotype is of great importance, specifically the development of models for the association between the presence of certain mutations and the resulting physical traits.
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Prior the data integration step, a semantic model is to be developed in order to define the main concepts, and their relationships, to be stored in the system. The data integration step is to be supported by an information system based on Semantic Web technologies, which will include the development of an ontology at the base of the data mapping.
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The first step towards the concretization of the objective of the HCM project consists in the integration of the genotype and phenotype data necessary for the characterization of HCM patients. The genotype data corresponds to the presence of the known HCM-associated mutations in the genome of the patients, while the phenotype data corresponds to the clinical elements upon which the clinicians rely to provide a diagnose. The latter normally include the results from physical examinations (e.g. electrocardiogram, echocardiogram), as well as the clinical history of the individual (e.g. age at diagnosis, sudden deaths in the family).
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After the integration of the data, its characteristics are to be explored in order to optimize the utilization of data mining algorithms to identify correlations between the patient genotype and its’ phenotype. Already existent data mining algorithms are to be used, and new ones are to be developed, according to their ability to deal with high-dimensionality data.
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The approach we propose to follow is based on [http://www.w3.org/standards/semanticweb/ Semantic Web] technologies, previously identified as suitable for the integration of heterogeneous data since they make it possible to integrate, share and reuse data in an application- and domain-independent manner.
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We are currently developing a semantic model in the [http://www.w3.org/standards/techs/owl#w3c_all Web Ontology Language] (OWL), which will identify the concepts, and relationships between concepts, underlying the data with which HCM is characterized, as well as its integration.
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The second step in the prosecution of the HCM project consists in the analysis of the integrated data in order to infer the previously referred genotype-phenotype correlations. Such analysis will be performed with a combination of powerful data mining techniques, such as support vector machines, with less powerful but more expressive techniques, such as decision trees. This dual approach is intended to provide accurate results without sacrificing their interpretation.
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The identified correlation patterns will be included in the semantic model and ultimately used in the HCM characterization system: upon introduction of a new patient’s data, the system will provide the medical doctor with the possible disease outcome for that particular patient.
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* Period: 1-Jan-2009 to 31-Dec-2012
* Period: 1-Jan-2009 to 31-Dec-2012
* Funding:  
* Funding:  
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** SFRH/BD/65257/2009, Doctoral research scholarship for [[Catia Machado]]
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** SFRH/BD/65257/2009, Doctoral research scholarship for [[Catia M. Machado]]

Revision as of 13:38, 14 September 2010

The objective of the HCM project consists in the integration and exploration of clinical and biological data, with the purpose of developing a patient characterization and prognostic system for the disease Hypertrophic cardiomyopathy (HCM).

HCM is a relatively common genetic myocardial disorder and the most frequent cause of sudden cardiac death in young people and athletes. It is characterized by a variable clinical presentation and onset, as well as a genetic heterogeneity denoted by 640 known mutations in more than 20 genes. Although the existence of a single mutation is sufficient for a positive diagnosis, the severity of HCM may not be the same for two individuals, even if direct relatives, since the presence of a given mutation can have a benign pattern in one individual and result in sudden cardiac death in another. Given the disease characteristics just referred, the identification of correlations between genotype and phenotype is of great importance, specifically the development of models for the association between the presence of certain mutations and the resulting physical traits.

The first step towards the concretization of the objective of the HCM project consists in the integration of the genotype and phenotype data necessary for the characterization of HCM patients. The genotype data corresponds to the presence of the known HCM-associated mutations in the genome of the patients, while the phenotype data corresponds to the clinical elements upon which the clinicians rely to provide a diagnose. The latter normally include the results from physical examinations (e.g. electrocardiogram, echocardiogram), as well as the clinical history of the individual (e.g. age at diagnosis, sudden deaths in the family).

The approach we propose to follow is based on Semantic Web technologies, previously identified as suitable for the integration of heterogeneous data since they make it possible to integrate, share and reuse data in an application- and domain-independent manner. We are currently developing a semantic model in the Web Ontology Language (OWL), which will identify the concepts, and relationships between concepts, underlying the data with which HCM is characterized, as well as its integration.

The second step in the prosecution of the HCM project consists in the analysis of the integrated data in order to infer the previously referred genotype-phenotype correlations. Such analysis will be performed with a combination of powerful data mining techniques, such as support vector machines, with less powerful but more expressive techniques, such as decision trees. This dual approach is intended to provide accurate results without sacrificing their interpretation.

The identified correlation patterns will be included in the semantic model and ultimately used in the HCM characterization system: upon introduction of a new patient’s data, the system will provide the medical doctor with the possible disease outcome for that particular patient.


Research Team

Funding


  • Period: 1-Jan-2009 to 31-Dec-2012
  • Funding:


Image:fct_logo.gif

Publications


| BibTeX source
Catia M. Machado, Ana T. Freitas, Francisco Couto, Enrichment analysis applied to disease prognosis. Em: M. Boeker, H. Herre, R. Hoehndorf, F. Loebe (Ed.), 4th Workshop on Ontologies in Biomedicine and Life Sciences (OBML) September, 2012.

Document | BibTeX source
Catia M. Machado, Francisco Couto, Alexandra R. Fernandes, Susana Santos, Ana T. Freitas, Toward a Translational Medicine Approach for Hypertrophic Cardiomyopathy.3rd International Conference on Information Technology in Bio- and Medical Informatics (ITBAM 2012) 2012. Springer-Verlag GmbH Berlin Heidelberg.

Document | BibTeX source
Catia M. Machado, Francisco Couto, Alexandra R. Fernandes, Susana Santos, Nuno Cardim, Ana T. Freitas, Semantic Characterization of Hypertrophic Cardiomyopathy Disease.First Workshop on Knowledge Engineering, Discovery and Dissemination in Health (KEDDH10), held in conjunction with The IEEE International Conference on Bioinformatics % Biomedicine (BIBM 2010) 2010.

| BibTeX source
Catia M. Machado, Francisco Couto, Alexandra R. Fernandes, Susana Santos, Nuno Cardim, Ana T. Freitas 2010: Unraveling Hypertrophic Cardiomyopathy Variability. ERCIM News 82 - Special Theme: Computational Biology.


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