Supplementary data for Reliable prediction of short linear motifs in the human proteome

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Authors: Rita Pancsa, Erzsébet Fichó, E. Zsofia Kalman, Csongor Gerdán, István Reményi, András Zeke, Gábor Tusnády, László Dobson
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Abstract

SData 1: Prediction results on the core ‘inside’ motifs (convolutional NNs, residue level prediction) SData 2: Prediction results on the core ‘inside’ motifs (fully connected NNs, residue level prediction) SData 3: Prediction results on the flanking regions of ‘inside’ motifs (convolutional NNs, residue level prediction) SData 4: Prediction results on the flanking regions of ‘inside’ motifs (fully connected NNs, residue level prediction) SData 5: Prediction results on the core ‘outside’ motifs (convolutional NNs, residue level prediction) SData 6: Prediction results on the core ‘outside’ motifs (fully connected NNs, residue level prediction) SData 7: Prediction results on ‘inside’ motifs (motif level prediction) SData 8: Prediction results on ‘outside’ motifs (motif level prediction)

Fields of Study & Subjects

Machine Learning in Bioinformatics Protein Structure and Dynamics vaccines and immunoinformatics approaches

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
26 February 2026
Language
EN