In this paper, we provide an introduction to machine learning tasks that address important
problems in genomic medicine. One of the goals of genomic medicine is to determine how …

This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

The task of protein sequence design is central to nearly all rational protein engineering
problems, and enormous effort has gone into the development of energy functions to guide …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

When clustering a dataset, the right number k of clusters to use is often not obvious, and
choosing k automatically is a hard algorithmic problem. In this paper we present an …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …

Increased availability of large repositories of chemical compounds is creating new
challenges and opportunities for the application of machine learning methods to problems in …

In many applications of graphical models arising in computer vision, the hidden variables of
interest are most naturally specified by continuous, non-Gaussian distributions. There exist …

Inference for probabilistic graphical models is still very much a practical challenge in large
domains. The commonly used and effective belief propagation (BP) algorithm and its …