Author

J. Ule

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#artificial intelligence Preprint Sep 2026

SeqMaestro: From nucleotide sequences to biological hypotheses through interpretable machine learning

SeqMaestro is a machine learning framework that proposes biological hypotheses from nucleotide sequences using interpretable models using interpretable models to make interpretable sequence analysis accessible to researchers without requiring extensive programming or machine learning expertise.

Evgeny S. Saveliev, Krzysztof Kacprzyk, Charlotte Capitanchik et al. · 0 citations

Open access Aug 2026

PARNET: A CLIP-SEQ-BASED FOUNDATION MODEL FOR RNA SEQUENCE REPRESENTATION LEARNING

Parnet is a multi-task foundation model trained end-to-end on 223 eCLIP-seq experiments spanning 150 RBPs to predict base-resolution RBP binding profiles directly from RNA sequence, and establishes the RBP interactome as a compact, functionally sufficient, and interpretable basis for foundation model pretraining in RNA...

Lambert Moyon, Andreina Tirabassi, Artem Baranowskii et al. · bioRxiv · 0 citations