Drugs and treatments could be precisely tailored to an individual patient by extracting their
cellular-and molecular-level information. For this approach to be feasible on a global scale …

A detailed overview of current research in kernel methods and their application to
computational biology. Modern machine learning techniques are proving to be extremely …

Global analyses of RNA expression levels are useful for classifying genes and overall
phenotypes. Often these classification problems are linked, and one wants to find “marker …

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on
statistical learning theory, and involves a training phase in which the model is trained by a …

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have
become methods of choice to characterize the human metabolome and MS-based …

We show that the support vector machine (SVM) classification algorithm, a recent
development from the machine learning community, proves its potential for structure–activity …

Motivation: Cancer diagnosis is one of the most important emerging clinical applications of
gene expression microarray technology. We are seeking to develop a computer system for …

The learning speed of classical Support Vector Regression (SVR) is low, since it is
constructed based on the minimization of a convex quadratic function subject to the pair …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

The recent successes of genome-wide expression profiling in biology tend to overlook the
power of genetics. We here propose a merger of genomics and genetics into 'genetical …