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Protein structure and fold prediction using tree-augmented naive Bayesian classifier
2004
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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This paper presents a framework using the Tree-Augmented Networks (TAN) based on the theory of learning Bayesian networks but with less restrictive assumptions than the naive Bayesian networks. ...
For determining the structure class and fold class of Protein Structure, computer-based techniques have became essential considering the large volume of the data. ...
This paper designs a framework called BAYESPROT with discretization of feature space and Tree-Augmented Network (TAN) Bayesian classifier as foundation to address the problem of structure and fold classification ...
pmid:14992519
fatcat:vrkxjou6qzeadlnidmeuw7qcxi
PROTEIN STRUCTURE AND FOLD PREDICTION USING TREE-AUGMENTED NAÏVE BAYESIAN CLASSIFIER
2005
Journal of Bioinformatics and Computational Biology
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For determining the structure class and fold class of Protein Structure, computerbased techniques have became essential considering the large volume of the data. ...
This paper presents a framework using the Tree-Augmented Networks (TAN) based on the theory of learning Bayesian networks but with less restrictive assumptions than the naïve Bayesian networks. ...
This paper designs a framework called BAYESPROT with discretization of feature space and Tree-Augmented Network (TAN) Bayesian classifier as foundation to address the problem of structure and fold classification ...
doi:10.1142/s0219720005001302
pmid:16078362
fatcat:qvmi5tnu3radbnnwj2uqmasuzu
Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks
2015
Advances in Bioinformatics
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Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. ...
The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes ...
Acknowledgments Data collection and sharing of this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( ...
doi:10.1155/2015/639367
pmid:26366461
pmcid:PMC4561111
fatcat:jsrmx6obkjal7pabpabidwyvve
ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins
[chapter]
2013
Lecture Notes in Computer Science
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The task of predicting protein functions using computational techniques is a major research area in the field of bioinformatics. ...
The ensemble is built using ABC-Miner, our recently introduced Ant-based Bayesian Classification algorithm. We use different types of protein representations to learn different classification models. ...
Carlos Silla for extracting the feature sets used in our experiments and Dr. Joao Pedro de Magalhaes for his valuable advice about the creation of the ageing-related protein's dataset. ...
doi:10.1007/978-3-642-37189-9_8
fatcat:jsek2syyizej5dgothhq7qkd7m
Protein Attributes Microtuning System (PAMS): an effective tool to increase protein structure prediction by data purification
2007
2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference
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The performance of each different classifier is also evaluated and discussed. In this paper a protein set of 232 protein chains are proposed to be used in the prediction. ...
By focusing on improvements in data quality and validation, our experiments yielded a highest prediction accuracy of protein secondary structure of 90.97%. ...
ACKNOWLEDGMENT We would like to thank Dr Lee Gillam and Mr. ...
doi:10.1109/dest.2007.372039
fatcat:ff4vostkd5er3o4qdm6hsbasq4
A Bayesian network approach for modeling local failure in lung cancer
2011
Physics in Medicine and Biology
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With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. ...
Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable ...
Patricia Lindsay and Dr. Andrew Hope for their help with collecting the retrospective data. This work was supported by NIH K25CA128809 and Fast Foundation grants. ...
doi:10.1088/0031-9155/56/6/008
pmid:21335651
pmcid:PMC4646092
fatcat:6stqluy5pbeh7e6grcsedcnm2e
An Integrated Approach to Learning Bayesian Networks of Rules
[chapter]
2005
Lecture Notes in Computer Science
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We evaluate two structure learning algorithms Naïve Bayes and Tree Augmented Naïve Bayes. ...
Each candidate rule is introduced into the network, and scored by whether it improves the performance of the classifier. We call the algorithm SAYU for Score As You Use. ...
We would like to thank Mark Goadrich and Rich Maclin for reading over drafts of this paper. ...
doi:10.1007/11564096_13
fatcat:66yxm55tvbgtdgmif33nxe3hsa
IoMT-based Association Rule Mining for the prediction of Human Protein Complexes
2020
IEEE Access
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to predict these structures. ...
., probabilistic Bayesian Network (BN), and Random Forest, in terms of accuracy and efficiency in addition to provide privacy. ...
In the statistical prediction, independent data set test, K-fold cross validation test, and jackknife cross-validation are usually used to assess the prediction capability of the model. ...
doi:10.1109/access.2019.2963797
fatcat:pakb6arbxbeyzbuawdniepkwye
Bayesian networks classifiers for gene-expression data
2011
2011 11th International Conference on Intelligent Systems Design and Applications
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In this work, we study the application of Bayesian networks classifiers for gene expression data in three ways: first, we made an exhaustive state-of-art of Bayesian classifiers and Bayesian classifiers ...
Third, we evaluate different Bayesian classifiers for this kind of data, including the C-RPDAG classifier presented by the authors. ...
ACKNOWLEDGEMENT This work has been supported by Spanish research programme Consolider Ingenio 2010, under project MIPRCV:CSD2007-00018, and the Spanish Ministry of Science and Innovation, under project ...
doi:10.1109/isda.2011.6121822
dblp:conf/isda/CamposCCM11
fatcat:ijh5jeu4tvfe3jwg3oczcincnm
Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS
2005
Journal of Biomedical Informatics
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In this paper, filter and wrapper approaches based on the feature subset selection are adapted to induce Bayesian classifiers (naive Bayes, selective naive Bayes, semi naive Bayes, tree augmented naive ...
Bayes, and k-dependence Bayesian classifier) and are applied to distinguish between the two subgroups of cirrhotic patients. ...
This work is partially supported by the Ministry of Science and Technology, by the Fondo ...
doi:10.1016/j.jbi.2005.05.004
pmid:15967731
fatcat:ubf6ljmodfchjh5vqjl5owtjie
Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices
2015
Molecular Biosystems
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RFCoil is a novel predictor for parallel coiled-coil dimer and trimer. ...
Prediction performance on the 10-fold cross-validation tests using the PrOCoil dataset We performed 10-fold cross-validation tests to assess the performance of the predictive models of RFCoil using the ...
using Bayesian variable selection response probabilities, can predict multiple oligomerization states for coiled-coil regions such as parallel dimer, antiparallel dimer, trimer and tetramer. 4 Therefore ...
doi:10.1039/c4mb00569d
pmid:25435395
fatcat:chvdwhzwuzdd5ahzogcvjxeop4
Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies
2010
BMC Bioinformatics
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We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences. ...
Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. ...
A review by Caflisch [17] classified the computational approaches used in predicting protein and peptide aggregation propensity into two general groups. ...
doi:10.1186/1471-2105-11-79
pmid:20144194
pmcid:PMC3098112
fatcat:6vklvbgw7ndhhlmc7dpa55u5jy
Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information
2014
BioMed Research International
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The recognition of protein folds is an important step for the prediction of protein structure and function. ...
After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (30960090, 31260203), The "CHUN HUI" Plan of Ministry of Education, and Talent Development Foundation of Inner ...
doi:10.1155/2014/262850
pmid:25136571
pmcid:PMC4127253
fatcat:o2v2kcjfqnaxvk7brmrpme75y4
Prediction of protein structural class using novel evolutionary collocation-based sequence representation
2008
Journal of Computational Chemistry
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Knowledge of structural classes is useful in understanding of folding patterns in proteins. ...
We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. ...
proteins, 5,6 prediction of folding rates, 7 protein fold prediction, 8 secondary structure content prediction, 9, 10 reduction of the conformation search space, 11 and for implementation of a heuristic ...
doi:10.1002/jcc.20918
pmid:18293306
fatcat:ug3s6uqnvfhuzdqauscsiauq6i
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers
2008
Computer Methods and Programs in Biomedicine
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In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. ...
Feature selection and boot-Bayesian network classifiers strap resampling add reliability and robustness to the overall process removing the false Robust arc identification positive findings. ...
structures of a naive Bayes classification model (left) with n predictive variables and a possible tree augmented naive Bayes (right) with four predictive variables. ...
doi:10.1016/j.cmpb.2008.02.010
pmid:18433926
fatcat:pjplkow2lfamjmkygepkhx7qgu
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