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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

An overview of how the information retrieval model will be applied to the DLBCL use case. Left side: After specifying the desired query parameters (Context, Quality Filtration), the system will return a potentially large result set of molecular medicine modality objects. This enumerated set of objects is the raw result. Please refer to the subsection “Model: Objects, Indexing Scheme and Queries,” last two paragraphs. Right side: One or more subsets of the raw result may then be selected by the user for visualization and organization based on the relationships between these objects. The subsection “Model: Object Relationships and Quality Filters” elaborates on this process. The full details of the DLBCL use case are mentioned in the subsection “Proof of Concept: Diffuse Large B C-Cell Lymphoma”. Three subsets of objects from the DLBCL domain along with their relationships are organized pictorially according to our model in Figures 2, 3 and 4.
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f1-cin-08-01: An overview of how the information retrieval model will be applied to the DLBCL use case. Left side: After specifying the desired query parameters (Context, Quality Filtration), the system will return a potentially large result set of molecular medicine modality objects. This enumerated set of objects is the raw result. Please refer to the subsection “Model: Objects, Indexing Scheme and Queries,” last two paragraphs. Right side: One or more subsets of the raw result may then be selected by the user for visualization and organization based on the relationships between these objects. The subsection “Model: Object Relationships and Quality Filters” elaborates on this process. The full details of the DLBCL use case are mentioned in the subsection “Proof of Concept: Diffuse Large B C-Cell Lymphoma”. Three subsets of objects from the DLBCL domain along with their relationships are organized pictorially according to our model in Figures 2, 3 and 4.

Mentions: A query to the knowledgebase should then return a subset of the objects in the knowledgebase. A simple enumeration of Papers, Algorithms, Datasets, and Models that relate to gene expression microarrays in the context of DLBCL is shown in the left side of Figure 1. We also realized that a query can be represented as a partial or complete Context. For example, the Contexts represented by the example queries above are shown in Table 1. Queries 1–3 specify partial Contexts, and Query 4 specifies a complete Context. A quick and simple indexing scheme can be achieved by using a set of canonical terms for each of the Context elements, and then indexing each of the objects with at least one complete Context tuple. Objects are retrieved when their Context elements match the Context elements specified in the query.


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)

An overview of how the information retrieval model will be applied to the DLBCL use case. Left side: After specifying the desired query parameters (Context, Quality Filtration), the system will return a potentially large result set of molecular medicine modality objects. This enumerated set of objects is the raw result. Please refer to the subsection “Model: Objects, Indexing Scheme and Queries,” last two paragraphs. Right side: One or more subsets of the raw result may then be selected by the user for visualization and organization based on the relationships between these objects. The subsection “Model: Object Relationships and Quality Filters” elaborates on this process. The full details of the DLBCL use case are mentioned in the subsection “Proof of Concept: Diffuse Large B C-Cell Lymphoma”. Three subsets of objects from the DLBCL domain along with their relationships are organized pictorially according to our model in Figures 2, 3 and 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-08-01: An overview of how the information retrieval model will be applied to the DLBCL use case. Left side: After specifying the desired query parameters (Context, Quality Filtration), the system will return a potentially large result set of molecular medicine modality objects. This enumerated set of objects is the raw result. Please refer to the subsection “Model: Objects, Indexing Scheme and Queries,” last two paragraphs. Right side: One or more subsets of the raw result may then be selected by the user for visualization and organization based on the relationships between these objects. The subsection “Model: Object Relationships and Quality Filters” elaborates on this process. The full details of the DLBCL use case are mentioned in the subsection “Proof of Concept: Diffuse Large B C-Cell Lymphoma”. Three subsets of objects from the DLBCL domain along with their relationships are organized pictorially according to our model in Figures 2, 3 and 4.
Mentions: A query to the knowledgebase should then return a subset of the objects in the knowledgebase. A simple enumeration of Papers, Algorithms, Datasets, and Models that relate to gene expression microarrays in the context of DLBCL is shown in the left side of Figure 1. We also realized that a query can be represented as a partial or complete Context. For example, the Contexts represented by the example queries above are shown in Table 1. Queries 1–3 specify partial Contexts, and Query 4 specifies a complete Context. A quick and simple indexing scheme can be achieved by using a set of canonical terms for each of the Context elements, and then indexing each of the objects with at least one complete Context tuple. Objects are retrieved when their Context elements match the Context elements specified in the query.

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