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Introduction to the development and validation of predictive biomarker models from high-throughput data sets
1HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, NY, USA.
Methods in Molecular Biology (Clifton, N.J.)
|July 24, 2010
Summary
This chapter details biomarker model development for predicting sample information using high-throughput data. It covers computational steps and presents a gene expression example using the open-source BDVal software.
Area of Science:
- Computational biology
- Biostatistics
- Machine learning
Background:
- High-throughput technologies generate large datasets from biological/clinical samples.
- These datasets enable biomarker discovery and predictive model development.
- Biomarker model development integrates statistics and machine learning.
Purpose of the Study:
- To discuss computational steps in biomarker model development.
- To review common approaches for each step.
- To present a practical example using gene expression data.
Main Methods:
- Mining large datasets for biomarker discovery.
- Developing and evaluating predictive statistical models.
- Utilizing the open-source BDVal software suite for analysis.
Main Results:
- A comprehensive overview of the computational workflow for biomarker model development.
- Demonstration of biomarker model development on a large gene expression dataset.
- Practical application of statistical and machine learning methods.
Conclusions:
- Biomarker model development is crucial for predicting sample information from high-throughput data.
- The BDVal suite offers a practical, open-source solution for this process.
- Integration of computational methods facilitates biomarker discovery and model building.