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

A systematic classification and accepted nomenclature of neuron types is much needed but
is currently lacking. This article describes a possible taxonomical solution for classifying …

In recent years, a plethora of approaches have been proposed to deal with the increasingly
challenging task of multi‐output regression. This study provides a survey on state‐of‐the‐art …

We have had to wait over 30 years since the naive Bayes model was first introduced in
1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, …

Multi-dimensional classification aims at finding a function that assigns a vector of class values
to a given vector of features. In this paper, this problem is tackled by a general family of …

Machine tool automation is an important aspect for manufacturing companies facing the
growing demand of profitability and high quality products as a key for competitiveness. The …

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity.
Past attempts to do so using anatomical, physiological or molecular features of cortical …

Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains. …

Bayesian networks are a type of probabilistic graphical models lie at the intersection between
statistics and machine learning. They have been shown to be powerful tools to encode …

As artificial intelligence is being increasingly used for high-stakes applications, it is becoming
more and more important that the models used be interpretable. Bayesian networks offer a …