Evolutionary Distances in the Twilight Zone—A Rational Kernel Approach

Evolutionary Distances in the Twilight Zone—A Rational Kernel Approach

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biol...

The Kernel Trick for Distances

A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as norm-based distances in Hilbert spaces. It turns out that common kernel algorithms, such as SVMs and kernel PCA, ...

Evolutionary distances

An Evolutionary Approach to Automatic Kernel Construction

Kernel-based learning presents a unified approach to machine learning problems such as classification and regression. The selection of a kernel and associated parameters is a critical step in the application of any kernel-based method to a problem. This paper presents a data-driven evolutionary approach for constructing kernels, named KTree. An application of KTree to the Support Vector Machine...

Estimating Evolutionary Distances between Sequences

These notes accompany a lecture at the summer school on mathematics for bioinformatics, Centre de Recherches Mathematiques, Montreal, August 2003. I develop basic Markov models for sequence evolution and show how these may be used to estimate evolutionary divergences between sequences. I then discuss extensions of the basic model to situations where the evolutionary rate varies over time and fo...