Aug 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 36 references
TL;DR
A novel model SSIPIN is proposed for protein function prediction, using three types of data including protein sequences, protein-protein interaction networks (PPIN) and protein tertiary structures, which outperforms existing methods on multiple datasets.
SPPIPred, an advanced machine learning-based model designed for precise PPI prediction, is presented, offering valuable insights to researchers in the field of bioinformatics and improving applications within bioengineering and pharmaceutical development.
M. Rahman, M. Ali, Md. Shohidullah et al.· PLoS ONE· 0 citations
The Port of Protein-Protein Interactomes (POPPIN), an integrated database dedicated to sharing experimentally verified PPIs and functional clues in rice, is constructed and serves as the first large-scale, experiment-based database for searchable PPIs in rice, and has the potential to be extended to other species under...
AlphaFunctor is a category theory based foundation model-like platform to bridge the gap between protein function annotation and property prediction, based on the hypothesis that protein function and properties can be directly derived from protein sequence.
Xiang Liu, Anna E. Yee, J. Vermaas et al.· arXiv.org· 0 citations
A neural network-based pipeline that integrates amino acid sequences with structural features is developed and provides a modular prototype for follow-up, more extensive protein modeling, including larger proteins and sequence of variable sizes.
Carl David Jasper Causin, M. Fyta· APL Machine Learning· 0 citations
Whether AlphaFold 3 complex prediction, combined with STRING evidence and domain-level analysis of interfaces and interaction partners, can help identify and characterize DUF-containing proteins and suggest roles for DUF4130 in nucleic-acid-associated radical-SAM biology and DUF5819 in a bacterial system related to vit...
Lino Riepenhausen, Francesco Costa, Antonina Andreeva et al.· bioRxiv· 0 citations
The autoencoder framework encodes protein sequence information related to domains, families, and patterns—into a lengthy, sparse binary vector that outperforms other neural network models, including convolutional neural networks, recurrent neural networks, long short-term memory networks, and bidirectional long short-t...
Biswajit Senapati, Ranjita Das· Journal of Computer-Aided Mo...· 0 citations
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