Predicting protein-protein interactions using signature products

By Shawn Martin, Diana C. Roe, Jean-Loup Faulon. Bioinform., 2005

390 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/76bf02ab4efddec949ad8a9cfc6aec28d090d99f

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In Molecular Biology

Citations in the corpus

Supervised classification and mathematical optimization (2013, background): “…and Medicine, such as classification of gene expression data [102] [245], homology detection [143], protein-protein interaction prediction [28] [171], abnormal brain activity classification [55] and cancer diagnosis [110] [165]; Machine Vision [75] [188]; Agriculture [179]; or Chemistry [60],…”
Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences (2008, methodology): “Several sequence-based methods (21–25) have shown that the information of amino acid sequences alone may be sufficient to identify novel PPIs, but the highest accuracy of these methods is only � 80%, such as the methods by Martin et al. ( 22 ), and Chou and Cai (25).”
Sequence-based prediction of protein protein interaction using a deep-learning algorithm (2017, methodology): “A previously generated dataset with 938 positive and 936 negative samples (2005 Martin dataset) [36] has been utilized in a number of previous studies [14, 37 – 39].”
Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners (2007, background): “This group of methods looks for the characteristic sequence or structural motifs which distinguish interacting proteins from non-interacting [71–74].”
Kernel methods for predicting protein-protein interactions (2005, methodology): “The pairwise sequence kernel was independently proposed in a recent paper (Martin et al., 2005) with a sequence representation by 3mers.”
Pairwise input neural network for target-ligand interaction prediction (2014, methodology): “In order to represent the binding site, we adopted the dictionary defined by Nagamine and Sakakibara [16], [17], which contains 4200 residue triplets of 199 types.”
Protein-protein interaction prediction via Collective Matrix Factorization (2010, methodology): “In this work, we used the Helicobacter pylori dataset as the target dataset and the Human dataset as the source dataset, both of which are also used in [28], [ 29 ], [30], [31], [12].”