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Developing computational model-based diagnostics to analyse clinical chemistry data.

Daniël B van Schalkwijk¹, Kees van Bochove, Ben van Ommen

¹U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl

Briefings in Bioinformatics

|January 9, 2010

Summary

Researchers can develop computational models for clinical chemistry diagnostics using metabolomics and proteomics data. This guide details model development, from design to evaluation, using a case study for lipoprotein disorders.

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Area of Science:

Computational biology
Clinical chemistry
Biomedical informatics

Background:

Novel metabolomics and proteomics technologies generate large, complex datasets.
Interpreting high-throughput biological data is challenging but crucial for diagnostics.
Computational models offer a pathway to interpret complex biological data for clinical applications.

Purpose of the Study:

To provide methodological and technical guidance for developing computational model-based diagnostics.
To address key considerations during the design, construction, and evaluation of diagnostic models.
To illustrate these considerations using a practical case study.

Main Methods:

Discussing critical issues in computational model development for clinical chemistry.
Utilizing the Particle Profiler tool development for lipoprotein disorders as a case study.
Presenting techniques for efficient model formulation, calculation, workflow structuring, and quality control.

Main Results:

The article outlines essential considerations for building robust computational diagnostic models.
The Particle Profiler case study demonstrates practical application of the discussed methodologies.
Techniques for optimizing model development, including quality control, are presented.

Conclusions:

Computational models are vital for translating complex omics data into clinical diagnostics.
Adhering to structured development and evaluation methodologies enhances model reliability.
This work provides a framework for researchers developing data-driven diagnostic tools in clinical chemistry.