Transformers-based models, such as BERT, have been one of the most successful deep
learning models for NLP. Unfortunately, one of their core limitations is the quadratic …

The tremendous amount of biological sequence data available, combined with the recent
methodological breakthrough in deep learning in domains such as computer vision or …

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 …

Highlights•Machine learning increasingly important for NGS.•Deep learning can improve
many NGS applications.•New AI techniques vital for life sciences.Next-generation …

Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is
a common task in genetics, mainly based on a genome similarity search of large genome …

In a general computational context for biomedical data analysis, DNA sequence
classification is a crucial challenge. Several machine learning techniques have used to …

Large-scale metagenomic assemblies have uncovered thousands of new species greatly
expanding the known diversity of microbiomes in specific habitats. To investigate the roles of …

Through the development of environmental DNA (eDNA) metabarcoding, in situ monitoring
of organisms is becoming easier and promises a revolution in our approaches to detect …

Although advancing the therapeutic alternatives for treating deadly cancers has gained
much attention globally, still the primary methods such as chemotherapy have significant …

Variable number tandem repeats (VNTRs) account for significant genetic variation in many
organisms. In humans, VNTRs have been implicated in both Mendelian and complex …