ArticleBioMed research international2016
Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Risk factor mining and prediction of urine protein progression in chronic kidney disease: a machine learning- based study.BMC medical informatics and decision making · 2023Article
- Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory.ACS omega · 2022Article
- The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models.BMC medical informatics and decision making · 2021Article
- Machine learning methods to predict amyloid positivity using domain scores from cognitive tests.Scientific reports · 2021Article
- Spectral analysis of molecular dynamics simulations on PDZ: MD sectors.Journal of biomolecular structure & dynamics · 2020Article
- Prediction of postoperative complications of pediatric cataract patients using data mining.Journal of translational medicine · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model (GNM), a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. We directly utilized the high frequency modes generated by GNM and further performed Gaussian Naive Bayes (GNB) to identify hot spot residues. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency. Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.