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Two important changes have been the ability to collect data on a high-throughput scale, and the ability to perform much more complex analysis using computational techniques.

New biomedical technologies like microarrays, next generation sequencers (for genomics) and mass spectrometry (for proteomics) generate enormous amounts of data, allowing many tests to be performed simultaneously.

Decision trees have the advantage that you can draw them and interpret them (even with a very basic understanding of mathematics and statistics).

Random Forests have thus been used for clinical decision support systems.

With a little intelligence, I can reach down and pick up big nuggets of gold.

And as long as I can do that, I'm not going to let any people in my department waste scarce resources in placer mining." Recent developments have made a large impact on biostatistics.

Due to high intercorrelation between the predictors (such as gene expression levels), the information of one predictor might be contained in another one.

It could be that only 5% of the predictors are responsible for 90% of the variability of the response.

For example, Gene Set Enrichment Analysis (GSEA) considers the perturbation of whole (functionally related) gene sets rather than of single genes.

These and other biostatisticians, mathematical biologists, and statistically inclined geneticists helped bring together evolutionary biology and genetics into a consistent, coherent whole that could begin to be quantitatively modeled.

In parallel to this overall development, the pioneering work of D'Arcy Thompson in On Growth and Form also helped to add quantitative discipline to biological study.

Careful analysis with biostatistical methods is required to separate the signal from the noise.

For example, a microarray could be used to measure many thousands of genes simultaneously, determining which of them have different expression in diseased cells compared to normal cells.

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