ArticleBioMed research international2022
BERT-PPII: The Polyproline Type II Helix Structure Prediction Model Based on BERT and Multichannel CNN.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- PolyProline Predictor: A web server for empirical sequence-based prediction of polyproline II helices.Protein science : a publication of the Protein Society · 2026Article
- Polyproline Modulates Membrane Translocation of Arginine-Rich Cell-Penetrating Peptides: Insights from Molecular Dynamics Simulations.The Journal of membrane biology · 2026Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model's
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Registered trials
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