In the post-genomic era, proteomics has achieved significant theoretical and practical
advances with the development of high-throughput technologies. Especially the rapid …

The prediction of peptide–protein or protein–protein interactions (PPI) is a challenging task,
especially if amino acid sequences are the only information available. Machine learning …

Decoding health and disease phenotypes is one of the fundamental objectives in
biomedicine. Whereas high-throughput omics approaches are available, it is evident that …

The prediction of protein complex in protein–protein interaction (PPI) networks plays such a
crucial role in the understanding of biological processes. This paper presents a moth–flame …

The rapid development of proteomics and high-throughput technologies has produced a
large amount of Protein-Protein Interaction (PPI) data, which makes it possible for …

Background Protein complex identification from protein-protein interaction (PPI) networks is
crucial for understanding cellular organization principles and functional mechanisms. In …

Background The accurate identification of protein complexes is important for the
understanding of cellular organization. Up to now, computational methods for protein …

Synthetic graph datasets are crucial for the assessment of network analysis algorithms,
providing a measure of their effectiveness and efficiency. However, most existing generation …

Detecting protein complexes from the Protein-Protein interaction network (PPI) is the
essence of discovering the rules of the cellular world. There is a large amount of PPI data …

Traditional researches predominantly rely on static protein network models, which present
highly averaged and idealized representations of protein networks. However, these models …