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 …

Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are
growing in popularity. Their versatility and modelling power is now employed across a …

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 …

The automatic induction of machine learning models capable of addressing supervised
learning, feature selection, clustering, and reinforcement learning problems requires …

In the field of protein engineering, the function and structure of proteins are key to
understanding cellular mechanisms, biological evolution, and biodiversity. With the …

The accurate prediction of a protein's secondary structure plays an increasingly critical role
in predicting its function and tertiary structure, as it is utilized by many of the current state‐of …

Protein function prediction is a complex multiclass multilabel classification problem,
characterized by multiple issues such as the incompleteness of the available annotations …

Discriminating outer membrane proteins (OMPs) from other folding types of globular and
membrane proteins is an important task both for identifying OMPs from genomic sequences …

Accurate protein secondary structure prediction plays an important role in direct tertiary
structure modeling, and can also significantly improve sequence analysis and sequence …