An integrative model for in-silico clinical-genomics discovery science.

Yves A Lussier, Indra Nell Sarkar, Michael Cantor

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Original languageEnglish (US)
Pages (from-to)469-473
Number of pages5
JournalProceedings / AMIA ... Annual Symposium. AMIA Symposium
StatePublished - 2002
Externally publishedYes

Fingerprint

Clinical Decision Support Systems
Genomics
Computer Simulation
Knowledge Bases
Computational Biology
Genes
Systematized Nomenclature of Medicine
Research
Molecular Medicine
Genetic Databases
Medical Informatics
Feasibility Studies
Human Genome
Cluster Analysis
Technology

Cite this

An integrative model for in-silico clinical-genomics discovery science. / Lussier, Yves A; Sarkar, Indra Nell; Cantor, Michael.

In: Proceedings / AMIA ... Annual Symposium. AMIA Symposium, 2002, p. 469-473.

Research output: Contribution to journalArticle

@article{5a90103fe24b449e81557c6b028755b2,
title = "An integrative model for in-silico clinical-genomics discovery science.",
abstract = "Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel {"}in-silico{"} clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.",
author = "Lussier, {Yves A} and Sarkar, {Indra Nell} and Michael Cantor",
year = "2002",
language = "English (US)",
pages = "469--473",
journal = "Proceedings / AMIA . Annual Symposium. AMIA Symposium",
issn = "1531-605X",
publisher = "Hanley & Belfus",

}

TY - JOUR

T1 - An integrative model for in-silico clinical-genomics discovery science.

AU - Lussier, Yves A

AU - Sarkar, Indra Nell

AU - Cantor, Michael

PY - 2002

Y1 - 2002

N2 - Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

AB - Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

UR - http://www.scopus.com/inward/record.url?scp=0036371279&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036371279&partnerID=8YFLogxK

M3 - Article

C2 - 12463868

AN - SCOPUS:0036371279

SP - 469

EP - 473

JO - Proceedings / AMIA . Annual Symposium. AMIA Symposium

JF - Proceedings / AMIA . Annual Symposium. AMIA Symposium

SN - 1531-605X

ER -