NDLI: Computational methods for coherent analysis of large-scale transcriptional, metabolic and histomorphometric profiles of a biological system: application to biomarker discovery

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Proceedings. Eighth International Conference on Information Visualisation

Technology For Life: North Carolina Symposium on Biotechnology and Bioinformatics - 2004

Technology for Life: North Carolina Symposium on Biotechnology and Bioinformatics - 2004 (IEEE Cat. No.04EX976)

North Carolina Symposium on Biotechnology and Bionformatics - 2004 - Copyright

NCSBB2004 Committee Members

Table of contents

Highly Automated DOE for Complex Biological Experiments - Tech 4 Life Conference

Ribosome tail ends as "signal detectors" for protein production in prokaryotes

Free energy based analysis of the coding region of Saccharomyces cerevisiae

Computational methods for coherent analysis of large-scale transcriptional, metabolic and histomorphometric profiles of a biological system: application to biomarker discovery

Healthcare and life sciences standards overview - Technology for Life: NC Symposium on Biotechnology and Bioinformatics

Changing risks with medical devices

Record integrity in bioinformatics 21 CFR Part II

Social implications of biotechnology

Relational learning from drug adverse events reports

MedfoLink: a powerful concept-based approach to medical report processing

Parametric modeling of brain signals

Emerging standards for interoperable biological systems

A practical multi-ontology approach to knowledge exploration

Telemedicine-based burn research initiative: longitudinal outcomes of patients

An SBIR & STTR program overview

Novel automated, grid based, web accessible thechnology for computer-aided drug discovery

Lessons learned from the NC biogrid project: building grids for competitive advantage in the biotech and pharmaceutical industries

Computational methods for coherent analysis of large-scale transcriptional, metabolic and histomorphometric profiles of a biological system: application to biomarker discovery

Content Provider	IEEE Xplore Digital Library
Copyright Year	2004
Abstract	Summary form only given. This paper describes computational inference methods for coherently analyzing three disparate and large-scale data streams for biomarker discovery. High-throughput biological approaches can assess the global molecular states of a living system by measuring the levels of thousands of transcribed genes (transcriptional profiles) and metabolites (metabolic profiles). However, effectively analyzing this information for understanding the disease pathways is a challenging problem. To address this problem a unique approach based on the concurrent analysis of gene expression, metabolic and histomorphometric data is developed. A novel machine vision based method was developed to assay the cellular state using quantitative tissue features. In this work we explore the formalization of biomarker discovery in a machine learning paradigm. In our approach the biomolecular and quantitative tissue profiles correspond to large and noisy descriptor space, and the classification attributes are defined by phenotypes. The relatively small number of samples compared to very high-dimensional and noisy descriptor space makes biomarker discovery an extremely challenging classification problem. The response of acetaminophen (APAP) in the rat liver was studied through transcriptional profiling using Affymetrix gene chips and metabolic profiling using liquid chromatography coupled with mass spectrometry (LC/MS). This paper describes the computational approach for the integrated metabolic, transcriptional, and quantitative tissue profiles elucidating the response of the rat liver to APAP exposure.
Sponsorship	IEEE Eastern North Carolina Section IEEE North Carolina Council IEEE Commun. Soc
File Size	65574
File Format	PDF
ISBN	0780388267
DOI	10.1109/SBB.2004.1364365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine vision Liver Biological systems Machine learning Biology computing Biomarkers Large-scale systems Gene expression Information analysis Diseases
Content Type	Text
Resource Type	Article

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