Predicting protein–protein interactions based only on sequences information

By Juwen Shen, Jian Zhang, Xiaomin Luo, Weiliang Zhu, Kunqian Yu, Kaixian Chen, Yixue Li, Hualiang Jiang. Proceedings of the National Academy of Sciences of the United States of America, 2007

957 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6148dff23e28e00bf97b51411a76a502d7aeb870

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Citations in the corpus

High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder (2015, background): “In particular, IDPs and IDRs were shown to be important for the protein–protein interactions (PPIs) and are enriched in the RNA- and DNA-binding proteins (1,11–16).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “• Edges can be predicted by the properties of their nodes, e.g. protein sequences, or domain structures (Smith & Sternberg, 2002; Li & Lai, 2007; Shen et al., 2007; Hue et al., 2010).”
Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions (2015, positive): “…have been widely used for predicting protein structural and functional classes, protein–protein interactions, subcellular locations and peptides of specific properties, etc (Chou et al., 2008; Rangwala et al., 2005; Shen et al., 2007; Ye et al., 2011; Zhang et al., 2005; Zhang et al., 2012).”
Sequence-based prediction of protein protein interaction using a deep-learning algorithm (2017, methodology): “However, most studies provided only the results of cross-validation, and did not test prediction results using external datasets [6, 10, 13, 14].”
A Survey on ensemble learning under the era of deep learning (2021, background): “2008), conjoint triad (CT) (Shen et al. 2007), local descriptor (LD) (Davies et al.”
Multitask Matrix Completion for Learning Protein Interactions Across Diseases (2016, neutral): “Shen et al. (2007) introduced the ‘‘conjoint triad model’’ for predicting PPIs using only amino acid sequences.”
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, background): “CTDD Composition (C), transition (T), and distribution (D) (39,40) CTriad Conjoint Triad (41) SOCNumber Sequence-Order-Coupling Number (42) QSOrder Quasi-sequence-order (43)”