Abstract Machine learning is increasingly important in microbiology where it is used for tasks
such as predicting antibiotic resistance and associating human microbiome features with …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Discriminative neural networks offer little or no performance guarantees when deployed on
data not generated by the same process as the training distribution. On such out-of …

False-positive identifications are a significant problem in metagenomics classification. We
present KrakenUniq, a novel metagenomics classifier that combines the fast k-mer-based …

Background The problem of supervised DNA sequence classification arises in several fields
of computational molecular biology. Although this problem has been extensively studied, it is …

Metagenomic shotgun sequencing data can identify microbes populating a microbial
community and their proportions, but existing taxonomic profiling methods are inefficient for …

Environmental DNA sequencing has revealed the expansive biodiversity of microorganisms
and clarified the relationship between host-associated microbial communities and host …

A major challenge in the analysis of environmental sequences is data integration. The
question is how to analyze different types of data in a unified approach, addressing both the …

The objective of this study is to make a comparison of the prediction performance of three
techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural …

Determining the taxonomic affiliation of sequences assembled from metagenomes remains
a major bottleneck that affects research across the fields of environmental, clinical and …