Statistical signals in bioinformatics

Statistical signals in bioinformatics.

The Arthur M. Sackler Colloquium of the National Academy of Sciences, "Frontiers in Bioinformatics: Unsolved Problems and Challenges," organized by David Eisenberg, Russ Altman, and myself, was held October 15-17, 2004, to provide a forum for discussing concepts and methods in bioinformatics serving the biological and medical sciences. The deluge of genomic and proteomic data in the last two de...

Bioinformatics to Biostochastics: Statistical Perspectives and Tasks Ahead

Bioinformatics is an emerging field of science emphasizing the application of mathematics, statistics, and informatics to study and analysis of very large molecular biological (mostly, genetic and genomic) systems (data sets). In a comparatively broader setup of large biological systems without necessarily having a predominant genetic undercurrent, and having genesis in biometry to biostatistic...

Statistical Learning and Kernel Methods in Bioinformatics

We briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. In addition, we present an overview of applications of kernel methods in bioinformatics.1 1 An Introductory Example In this Section, we formalize the problem of pattern recognition as that of classifying objects called “pattern” into one of two classes. We introduce a simple pa...

MSc in Bioinformatics: Statistical Data Mining

Challenges in Bioinformatics for Statistical Data Miners

Starting with possible definitions of statistical data mining and bioinformatics, this article will give a general side-by-side view of both fields, emphasising on the statistical data mining part and its techniques, illustrate possible synergies and discuss how statistical data miners may collaborate in bioinformatics’ challenges in order to unlock the secrets of the cell. What is statistics a...