Intuitively, this formula assigns higher prediction scores to nodes exhibiting a greater number of common neighbors, indicative of a higher probability for interaction. Moreover, through implementing the Resource Allocation (RA) algorithm [82], nodes with high degrees are subjected to penalties, ...
In this work we tackle the multi-level protein interaction prediction (MLPIP) problem, first introduced by Yip et al.[21], which requires to establish the binding state of all uncharacterized pairs of proteins, domains and residues. Contrary to standard protein–protein interaction prediction, the...
Mondal, "Springs: Prediction of protein-protein interaction sites using artificial neural networks," PeerJ PrePrints, Tech. Rep., 2014.G. Singh, K. Dhole, P. Pai, S. Mondal, SPRINGS: prediction of protein-protein interaction sites using artificial neural networks, J. Proteom. Comput. Biol....
Robust and accurate prediction of protein–protein interac- tions by exploiting evolutionary information. Sci Rep. 2021;11(1):1–12. 9. Zhang C, Freddolino PL, Zhang Y. COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information. ...
(2009). PIPs: human protein-protein interaction prediction database. Nucleic acids research, 37, D651-656. Article CAS PubMed Google Scholar Meireles LM, Dömling AS, Camacho CJ. (2010). ANCHOR: a web server and database for analysis of protein–protein interaction binding pockets for ...
Overview of machine learning and deep learning approaches to protein–protein interaction site prediction. Input structural or sequence data requires feature engineering or transformation into an appropriate representation for the architecture of the model ...
node features in a graphical representation of a protein. Theper-proteinembedding is generated by taking the mean across the protein sequence length. The generated embeddings for protein sequences are then fed to an MLP classifier for protein interaction prediction. The results are presented in ...
Transfer learning via multi-scale convolutional neural layers for human–virus protein–protein interaction prediction Bioinformatics, 37 (2021), pp. 4771-4778 Google Scholar [20] W. Liu-Wei, Ş. Kafkas, J. Chen, et al. DeepViral: prediction of novel virus–host interactions from protein seq...
The Supplementary Discussion is a comprehensive literature review of all 27 wild-type and mutant SARS-CoV-2 bait protein-protein interaction (PPI) subnetworks. Individual bait subnetwork images are provided at the front of the document for reference (and are zoomed view representations from Fig. 3...
Sequence-based prediction of protein–protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics. 2016;17(1):184. Article Google Scholar Hamp T, Rost B. Evolutionary profiles improve protein–protein interaction prediction from sequence. Bioinformatics....