Y
Yoichi Murakami
Researcher at Tohoku University
Publications - 38
Citations - 898
Yoichi Murakami is an academic researcher from Tohoku University. The author has contributed to research in topics: Medicine & Electronic structure. The author has an hindex of 12, co-authored 34 publications receiving 756 citations. Previous affiliations of Yoichi Murakami include Osaka University & University of Tokyo.
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Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites
Yoichi Murakami,Kenji Mizuguchi +1 more
TL;DR: A new method to predict interaction sites, i.e. residues binding to other proteins, in protein sequences, using a Naïve Bayes classifier and conditional probabilities of each sequence feature are estimated using a kernel density estimation method.
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SHARP2: protein--protein interaction predictions using patch analysis
Yoichi Murakami,Susan Jones +1 more
TL;DR: UNLABELLED SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures that implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein.
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Network analysis and in silico prediction of protein–protein interactions with applications in drug discovery
TL;DR: How PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research are discussed.
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PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences
TL;DR: The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences with an accuracy of 85%, specificity of 90% and a Matthews correlation coefficient of 0.41 and outperformed other publicly available servers.
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eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape.
TL;DR: A method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB), which generates a prediction result as a virtual complex structure.