DiseaseCard: A Web-Based Tool for the Collaborative Integration of Genetic and Medical Information

Oliveira, José Luís; Dias, Gaspar; Oliveira, Ilídio; Rocha, Patrícia; Hermosilla, I.; Vicente, Javier; Spiteri, Inmaculada; Martín-Sánchez, Fernando; Pereira, António Sousa

doi:10.1007/978-3-540-30547-7_41

Cited by 16 publications

(18 citation statements)

References 7 publications

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“…It also displays information on specialized consultations, diagnostics, research projects, clinical trials and support groups. Diseasecard [8] is another portal aggregating information regarding rare diseases and pointing to key elements for both the education and the biomedical research field.…”

Section: Overviewmentioning

confidence: 99%

A Rare Disease Patient Manager

Lopes

Mendonça

Rocha

et al. 2012

Advances in Intelligent and Soft Computing

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Abstract. The personal health implications behind rare diseases are seldom considered in widespread medical care. The low incidence rate and complex treatment process makes rare disease research an underrated field in the life sciences. However, it is in these particular conditions that the strongest relations between genotypes and phenotypes are identified. The rare disease patient manager, detailed in this manuscript, presents an innovative perspective for a patient-centric portal integrating genetic and medical data. With this strategy, patient's digital records are transparently integrated and connected to wet-lab genetics research in a seamless working environment. The resulting knowledge base offers multiple data views, geared towards medical staff, with patient treatment and monitoring data; genetics researchers, through a custom locus-specific database; and patients, who for once play an active role in their treatment and rare diseases research.

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Section: Overviewmentioning

confidence: 99%

A Rare Disease Patient Manager

Lopes

Mendonça

Rocha

et al. 2012

Advances in Intelligent and Soft Computing

Self Cite

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“…[7], [11], [12], [32], [33]), it is addressing the issues of informed consent and ethical approval, data protection, compliance with institutional, national and European regulations, and security [34]. In our view, the MammoGrid project paves the way for further research and development projects to meet the aims of the HealthGrid association [35] in the following respects:…”

Section: Future Directions and Conclusionmentioning

confidence: 99%

“…BIRN provides federated medical data, which enables a software 'fabric' for seamless and secure federation of data across the network and facilitates the collaborative use of domain tools and flexible processing/analysis frameworks for the study of Alzheimer's disease. The INFOGENMED initiative [11] has given the lead to projects in moving from genomic information to individualized healthcare using data distributed across Europe. Finally the CDSS [12] project is a system which uses knowledge extracted from clinical practice to provide a classification of patients' illnesses, implemented on a Grid platform.…”

Section: Introductionmentioning

confidence: 99%

Experiences of engineering Grid-based medical software

Estrella

Hauer

McClatchey

et al. 2007

International Journal of Medical Informatics

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SummaryObjectives: Grid-based technologies are emerging as potential solutions for managing and collaborating distributed resources in the biomedical domain. Few examples exist, however, of successful implementations of Grid-enabled medical systems and even fewer have been deployed for evaluation in practice. The objective of this paper is to evaluate the use in clinical practice of a Grid-based imaging prototype and to establish directions for engineering future medical Grid developments and their subsequent deployment. Method: The MammoGrid project has deployed a prototype system for clinicians using the Grid as its information infrastructure. To assist in the specification of the system requirements (and for the first time in healthgrid applications), use-case modelling has been carried out in close collaboration with clinicians and radiologists who had no prior experience of this modelling technique. A critical qualitative and, where possible, quantitative analysis of the MammoGrid prototype is presented leading to a set of recommendations from the delivery of the first deployed Grid-based medical imaging application. Results: We report critically on the application of software engineering techniques in the specification and implementation of the MammoGrid project and show that use-case modelling is a suitable vehicle for representing medical requirements and for communicating effectively with the clinical community. This paper also discusses the practical advantages and limitations of applying the Grid to real-life clinical applications and presents the consequent lessons learned. Conclusions:The work presented in this paper demonstrates that given suitable commitment from collaborating radiologists it is practical to deploy in practice medical imaging analysis applications using the Grid but that standardisation in and stability of the Grid software is a necessary pre-requisite for successful healthgrids. The MammoGrid prototype has therefore paved the way for further advanced Grid-based deployments in the medical and biomedical domains.

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“…On one hand, it is possible to find free biomedical vocabularies like Unified Medical Language System (UMLS) [2], Human Phenotype Ontology (HPO) [3], [4], Disease Ontology (DO) [5] or MeSH [6], all of them offering disease classifications, disease coding standards and associated medical resources. On the other hand, one can find bioinformatic databases created by complex medical system, like DiseaseCard [7]- [9], MalaCards [10]- [12], GeneCard [13], Diseases Database (DD)1, DISEASES [14], SIGnaling Network Open Resource (SIGNOR) [15], Kyoto Encyclopedia of Genes and Genomes (KEGG) [16], MENTHA [17], PhosphositePlus [18], PhosphoELM [18], UniProtKB [19], Human Gene Mutation Database (HGMD) [20] and Comparative Toxicogenomics Database (CTD) [21]. These datasets have generally been created by processing the information from several sources, and they usually offer simple search engines; yet, not all of them have a systematic and structured form of sharing their knowledge.…”

Section: Introductionmentioning

confidence: 99%

DISNET: A framework for extracting phenotypic disease information from public sources

García

Santamaría

Valle

et al. 2018

Preprint

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Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper, we present DISNET (disnet.ctb.upm.es), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and Mayo Clinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.

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DiseaseCard: A Web-Based Tool for the Collaborative Integration of Genetic and Medical Information

Cited by 16 publications

References 7 publications

A Rare Disease Patient Manager

A Rare Disease Patient Manager

Experiences of engineering Grid-based medical software

DISNET: A framework for extracting phenotypic disease information from public sources

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