Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Rapid technological advances have led to the production of different types of biological data
and enabled construction of complex networks with various types of interactions between …

Classification with imbalanced class distributions is a major problem in machine learning.
Researchers have given considerable attention to the applications in many real-world …

The increasing wealth of biological data coming from a large variety of platforms and the
continued development of new high-throughput methods for probing biological systems …

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

Motivation: Despite advances in high-throughput methods for discovering protein–protein
interactions, the interaction networks of even well-studied model organisms are sketchy at …

During the past 3 years, the support vector machine (SVM) learning algorithm has been
extensively applied within the field of computational biology. The algorithm has been used to …

This algorithm can be extended in various ways to provide linear time prediction cost in the
length of the sequence to be classified. We demonstrate extensions in the case of position …

A key issue in supervised protein classification is the representation of input sequences of
amino acids. Recent work using string kernels for protein data has achieved state-of-the-art …

The protein-protein interaction networks of even well-studied model organisms are sketchy
at best, highlighting the continued need for computational methods to help direct …