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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

A pictorial representation of the first three widely cited Papers relevant to the DLBCL use case along with the Datasets, Algorithms, and Models that were described in these Papers. Identifying and presenting relationships between these objects is important for the semantic organization of this domain. These relationships are represented by edges connecting the different objects. For example, the three Papers each describe how Algorithms were applied to Datasets to produce decision Models. We identify this class of ternary relationship as Run_ on_Produce (Produce in the figure for simplification). Furthermore, the Shipp (Shipp and others, 2002) and the Rosenwald (Rosenwald and others, 2002) Papers describe how the rightmost and leftmost predictive Models (respectively) were validated using the Datasets that they had assayed. This scenario is detailed in the subsection “Proof of Concept: Diffuse Large B-cell Lymphoma,” paragraphs 1–3.
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f2-cin-08-01: A pictorial representation of the first three widely cited Papers relevant to the DLBCL use case along with the Datasets, Algorithms, and Models that were described in these Papers. Identifying and presenting relationships between these objects is important for the semantic organization of this domain. These relationships are represented by edges connecting the different objects. For example, the three Papers each describe how Algorithms were applied to Datasets to produce decision Models. We identify this class of ternary relationship as Run_ on_Produce (Produce in the figure for simplification). Furthermore, the Shipp (Shipp and others, 2002) and the Rosenwald (Rosenwald and others, 2002) Papers describe how the rightmost and leftmost predictive Models (respectively) were validated using the Datasets that they had assayed. This scenario is detailed in the subsection “Proof of Concept: Diffuse Large B-cell Lymphoma,” paragraphs 1–3.

Mentions: We conducted a broad search for DLBCL gene-expression-related objects, by placing a query as in Figure 1 that specified the following Context: (Disease = DLBCL, Modality = Genomic). In the following section we will discuss three clinical bioinformatics scenarios that involve a subset of DLBCL gene-expression-related objects. The scenarios were encountered when we analyzed the set of manually collected objects that satisfied this Context. Figures 2–4 will provide a pictorial representation of these scenarios.


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)

A pictorial representation of the first three widely cited Papers relevant to the DLBCL use case along with the Datasets, Algorithms, and Models that were described in these Papers. Identifying and presenting relationships between these objects is important for the semantic organization of this domain. These relationships are represented by edges connecting the different objects. For example, the three Papers each describe how Algorithms were applied to Datasets to produce decision Models. We identify this class of ternary relationship as Run_ on_Produce (Produce in the figure for simplification). Furthermore, the Shipp (Shipp and others, 2002) and the Rosenwald (Rosenwald and others, 2002) Papers describe how the rightmost and leftmost predictive Models (respectively) were validated using the Datasets that they had assayed. This scenario is detailed in the subsection “Proof of Concept: Diffuse Large B-cell Lymphoma,” paragraphs 1–3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-08-01: A pictorial representation of the first three widely cited Papers relevant to the DLBCL use case along with the Datasets, Algorithms, and Models that were described in these Papers. Identifying and presenting relationships between these objects is important for the semantic organization of this domain. These relationships are represented by edges connecting the different objects. For example, the three Papers each describe how Algorithms were applied to Datasets to produce decision Models. We identify this class of ternary relationship as Run_ on_Produce (Produce in the figure for simplification). Furthermore, the Shipp (Shipp and others, 2002) and the Rosenwald (Rosenwald and others, 2002) Papers describe how the rightmost and leftmost predictive Models (respectively) were validated using the Datasets that they had assayed. This scenario is detailed in the subsection “Proof of Concept: Diffuse Large B-cell Lymphoma,” paragraphs 1–3.
Mentions: We conducted a broad search for DLBCL gene-expression-related objects, by placing a query as in Figure 1 that specified the following Context: (Disease = DLBCL, Modality = Genomic). In the following section we will discuss three clinical bioinformatics scenarios that involve a subset of DLBCL gene-expression-related objects. The scenarios were encountered when we analyzed the set of manually collected objects that satisfied this Context. Figures 2–4 will provide a pictorial representation of these scenarios.

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