miRBench: novel benchmark datasets for microRNA binding site prediction that mitigate against prevalent microRNA frequency class bias
| Autoři | |
|---|---|
| Rok publikování | 2025 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://academic.oup.com/bioinformatics/article/41/Supplement_1/i542/8199406?login=true |
| Doi | http://dx.doi.org/10.1093/bioinformatics/btaf233 |
| Klíčová slova | miRNA; THERAPEUTICS; RNAS |
| Popis | MicroRNAs (miRNAs) are crucial regulators of gene expression, but the precise mechanisms governing their binding to target sites remain unclear. A major contributing factor to this is the lack of unbiased experimental datasets for training accurate prediction models. While recent experimental advances have provided numerous miRNA–target interactions, these are solely positive interactions. Generating negative examples in silico is challenging and prone to introducing biases, such as the miRNA frequency class bias identified in this work. Biases within datasets can compromise model generalization, leading models to learn dataset-specific artifacts rather than true biological patterns. |
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