Genome-wide scans using small RNA-seq data often yield high rates of false-positive MIRNA annotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating new MIRNA annotations using sRNA-seq data.

Biological systems often evolve across multiple time scales, posing major challenges for constructing accurate models directly from data. Traditional model reduction techniques require explicit equations and thus cannot be applied when only observational data are available.

There is growing interest in pre-training antibody language models (AbLMs) with a mixture of unpaired and natively paired sequences, seeking to combine the proven benefits of training with natively paired sequences with the massive scale of unpaired antibody sequence datasets.