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A novel information retrieval model for high-throughput molecular medicine modalities.

Wehbe FH, Brown SH, Massion PP, Gadd CS, Masys DR, Aliferis CF - Cancer Inform (2009)

Bottom Line: Significant research has been devoted to predicting diagnosis, prognosis, and response to treatment using high-throughput assays.We first explain why this goal is inadequately supported by existing databases and portals and then introduce a novel semantic indexing and information retrieval model for clinical bioinformatics.The formalism provides the means for indexing a variety of relevant objects (e.g. papers, algorithms, signatures, datasets) and includes a model of the research processes that creates and validates these objects in order to support their systematic presentation once retrieved.We test the applicability of the model by constructing proof-of-concept encodings and visual presentations of evidence and modalities in molecular profiling and prognosis of: (a) diffuse large B-cell lymphoma (DLBCL) and (b) breast cancer.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. firas.wehbe@vanderbilt.edu

ABSTRACT
Significant research has been devoted to predicting diagnosis, prognosis, and response to treatment using high-throughput assays. Rapid translation into clinical results hinges upon efficient access to up-to-date and high-quality molecular medicine modalities. We first explain why this goal is inadequately supported by existing databases and portals and then introduce a novel semantic indexing and information retrieval model for clinical bioinformatics. The formalism provides the means for indexing a variety of relevant objects (e.g. papers, algorithms, signatures, datasets) and includes a model of the research processes that creates and validates these objects in order to support their systematic presentation once retrieved.We test the applicability of the model by constructing proof-of-concept encodings and visual presentations of evidence and modalities in molecular profiling and prognosis of: (a) diffuse large B-cell lymphoma (DLBCL) and (b) breast cancer.

No MeSH data available.


Related in: MedlinePlus

This figure depicts objects and object relationships that span the development and evolution of the MammaPrint™Model from its earlier versions. The figure also represents the validation of MammaPrint™ across multiple Datasets and its comparison to other Models. Notice that the other clinical predictive models are classical models that do not incorporate molecular data. The information retrieval framework will incorporate classical (non-molecular) clinical predictive Models only when they are relevant to the validation of molecular prediction Models. Otherwise classical Models will not be indexed or stored. Similar to the process described in Figure 1, a query to this domain will return a raw set of objects (Part I, left side). A subset of the raw result may be selected for visual organization and display (right side) of the objects and their relationships (Part II, right side). The detailed prose description of this scenario is presented in the subsection “Proof of Concept: Molecular Prognostic Test for Breast Cancer—MammaPrint®”.
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f5-cin-08-01: This figure depicts objects and object relationships that span the development and evolution of the MammaPrint™Model from its earlier versions. The figure also represents the validation of MammaPrint™ across multiple Datasets and its comparison to other Models. Notice that the other clinical predictive models are classical models that do not incorporate molecular data. The information retrieval framework will incorporate classical (non-molecular) clinical predictive Models only when they are relevant to the validation of molecular prediction Models. Otherwise classical Models will not be indexed or stored. Similar to the process described in Figure 1, a query to this domain will return a raw set of objects (Part I, left side). A subset of the raw result may be selected for visual organization and display (right side) of the objects and their relationships (Part II, right side). The detailed prose description of this scenario is presented in the subsection “Proof of Concept: Molecular Prognostic Test for Breast Cancer—MammaPrint®”.

Mentions: The same semantic representation and organizational principles of Papers, Datasets, Algorithms, and Models that relate to MammaPrint®, the first commercial Breast Cancer molecular prognostic test, are shown in Figure 5 and explained below.


A novel information retrieval model for high-throughput molecular medicine modalities.

Wehbe FH, Brown SH, Massion PP, Gadd CS, Masys DR, Aliferis CF - Cancer Inform (2009)

This figure depicts objects and object relationships that span the development and evolution of the MammaPrint™Model from its earlier versions. The figure also represents the validation of MammaPrint™ across multiple Datasets and its comparison to other Models. Notice that the other clinical predictive models are classical models that do not incorporate molecular data. The information retrieval framework will incorporate classical (non-molecular) clinical predictive Models only when they are relevant to the validation of molecular prediction Models. Otherwise classical Models will not be indexed or stored. Similar to the process described in Figure 1, a query to this domain will return a raw set of objects (Part I, left side). A subset of the raw result may be selected for visual organization and display (right side) of the objects and their relationships (Part II, right side). The detailed prose description of this scenario is presented in the subsection “Proof of Concept: Molecular Prognostic Test for Breast Cancer—MammaPrint®”.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2664697&req=5

f5-cin-08-01: This figure depicts objects and object relationships that span the development and evolution of the MammaPrint™Model from its earlier versions. The figure also represents the validation of MammaPrint™ across multiple Datasets and its comparison to other Models. Notice that the other clinical predictive models are classical models that do not incorporate molecular data. The information retrieval framework will incorporate classical (non-molecular) clinical predictive Models only when they are relevant to the validation of molecular prediction Models. Otherwise classical Models will not be indexed or stored. Similar to the process described in Figure 1, a query to this domain will return a raw set of objects (Part I, left side). A subset of the raw result may be selected for visual organization and display (right side) of the objects and their relationships (Part II, right side). The detailed prose description of this scenario is presented in the subsection “Proof of Concept: Molecular Prognostic Test for Breast Cancer—MammaPrint®”.
Mentions: The same semantic representation and organizational principles of Papers, Datasets, Algorithms, and Models that relate to MammaPrint®, the first commercial Breast Cancer molecular prognostic test, are shown in Figure 5 and explained below.

Bottom Line: Significant research has been devoted to predicting diagnosis, prognosis, and response to treatment using high-throughput assays.We first explain why this goal is inadequately supported by existing databases and portals and then introduce a novel semantic indexing and information retrieval model for clinical bioinformatics.The formalism provides the means for indexing a variety of relevant objects (e.g. papers, algorithms, signatures, datasets) and includes a model of the research processes that creates and validates these objects in order to support their systematic presentation once retrieved.We test the applicability of the model by constructing proof-of-concept encodings and visual presentations of evidence and modalities in molecular profiling and prognosis of: (a) diffuse large B-cell lymphoma (DLBCL) and (b) breast cancer.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. firas.wehbe@vanderbilt.edu

ABSTRACT
Significant research has been devoted to predicting diagnosis, prognosis, and response to treatment using high-throughput assays. Rapid translation into clinical results hinges upon efficient access to up-to-date and high-quality molecular medicine modalities. We first explain why this goal is inadequately supported by existing databases and portals and then introduce a novel semantic indexing and information retrieval model for clinical bioinformatics. The formalism provides the means for indexing a variety of relevant objects (e.g. papers, algorithms, signatures, datasets) and includes a model of the research processes that creates and validates these objects in order to support their systematic presentation once retrieved.We test the applicability of the model by constructing proof-of-concept encodings and visual presentations of evidence and modalities in molecular profiling and prognosis of: (a) diffuse large B-cell lymphoma (DLBCL) and (b) breast cancer.

No MeSH data available.


Related in: MedlinePlus