There is a pressing need for computational methods to move beyond data visualization and toward generating conclusions that are quantitatively supportable. Visual methods can help in the formation of hypotheses, but in general cannot lend quantitative support for the hypotheses. Such methods include many applications of clustering, principal components analysis, and multi-dimensional graphical exploration techniques. Statistical techniques can often provide quantitative support to hypotheses derived from visual analysis, but several aspects of the high-bandwidth data being currently produced can make it difficult to detect patterns that meet numerical tests of significance. However, biological systems are too complex for such methods to be broadly applicable.
Empirical methods seek to relate data to outcomes in a way that can be quantified for significance.
The empirical methods encompass pattern recognition, machine learning, neural networks, and multivariate function approximation. Relating DNA and protein sequences to protein function. Identifying small drug-like chemicals based on molecular structure that have potential value in cancer therapy. Extensive interdisciplinary interaction is required in developing methods that maximally utilize the complex, noisy data characteristic of biological systems. We benefit enormously from collaboration with the broad array of biological and clinical researchers here.