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Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks
2009
2009 International Conference of Soft Computing and Pattern Recognition
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Experimental results imply that RBF Neural Network holds better Correct Classification Rate (CCR) compared to other common classification methods such as MLP networks. ...
Our experiments also show that the Bayesian fusion method can improve the correct classification rate of proteins up to 20% with the final CCR of 59% by reducing both bias and variance error of the RBF ...
Artificial Neural Networks The neural network is a very applicable regression and classification tool which has the capability of representing complex relationships among inputs and outputs of a system ...
doi:10.1109/socpar.2009.91
dblp:conf/socpar/HashemiSN09
fatcat:34awm2regzditdc7nik7jhmqry
Adaptive machine learning for protein engineering
[article]
2021
arXiv
pre-print
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However, when using these models to suggest new protein designs, one must deal with the vast combinatorial complexity of protein sequences. ...
First, we discuss how to select sequences through a single round of machine-learning optimization. ...
B.L.H. acknowledges the support of the Stanford Science Fellows program. ...
arXiv:2106.05466v2
fatcat:x724wxzwkbfh5pjdpakthqmow4
Machine learning in bioinformatics
2006
Briefings in Bioinformatics
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A review of deterministic and stochastic heuristics for optimization in the same domain is presented. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown. ...
Applications in genomics, proteomics, systems biology, evolution and text mining are also shown. ...
Acknowledgements The authors are grateful to the anonymous reviewers for their comments, which have helped us to greatly improve this article. ...
doi:10.1093/bib/bbk007
pmid:16761367
fatcat:4oss26occvhkjnetcr3sesnkcu
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
2021
Bioinformatics
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Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. ...
Results At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. ...
The Bayesian neural network model is also a good predictor for an unbalanced dataset, a common problem in real drug-protein interaction applications. ...
doi:10.1093/bioinformatics/btab346
pmid:33978713
pmcid:PMC8545317
fatcat:3g5ivbdlevb2hpyoxdxusrsyxa
Bayesian analysis, pattern analysis, and data mining in health care
2004
Current Opinion in Critical Care
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Recent findings: Bayesian networks and other probabilistic graphical models are beginning to emerge as methods for discovering patterns in biomedical data and also as a basis for the representation of ...
Purpose of review: To discuss the current role of data mining and Bayesian methods in biomedicine and heath care, in particular critical care. ...
Neural networks and support vector machines A neural network is a computational representation that takes as input a sequence of numbers, say encoded patient features, and outputs another sequence of numbers ...
doi:10.1097/01.ccx.0000141546.74590.d6
pmid:15385759
fatcat:dodreho355hthmsjyf2c5b3qh4
Protein Secondary Structure Prediction using Bayesian Inference method on Decision fusion algorithms
2007
2007 IEEE International Parallel and Distributed Processing Symposium
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Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. ...
Previously research was performed in this field using several techniques such as neural networks, Simulated annealing (SA) and Genetic algorithms (GA) for improving the protein secondary structure prediction ...
Initially we feed the sequence profiles of amino acids into the profilebased neural network to predict the protein secondary structure. ...
doi:10.1109/ipdps.2007.370430
dblp:conf/ipps/AkkaladeviK07
fatcat:hugcawgrizfqbf5dhq6htiodae
Predicting secondary structure of Oxidoreductase protein family using Bayesian Regularization Feed-forward Backpropagation ANN Technique
2010
Journal of Proteomics & Bioinformatics
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The neural network has been trained using Bayesian Regularization Feed-forward Backpropagation Neural Network Technique to predict the -helix, -sheet and coil regions of this protein family. ...
Feed-forward neural network have been trained by analyzing windows of 25 parameters for predicting the central residue of protein sequence. PSI-BLAST has been used for multiple-sequence alignment. ...
Work is also under way to improve the sequence-to-structure prediction produced by the neural network and can be applied to other families of protein. ...
doi:10.4172/jpb.1000137
fatcat:umngwr3gr5gv7lc5j7jvtxys3q
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
[article]
2020
arXiv
pre-print
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Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. ...
The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. ...
All authors participated in the discussion of the results. Q.K and W.J wrote the manuscript. All authors reviewed the manuscript. ...
arXiv:2012.08194v2
fatcat:52iiq2wacjgstbjz7bwpdp5be4
Intelligent mining of large-scale bio-data: Bioinformatics applications
2017
Biotechnology & Biotechnological Equipment
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The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. ...
Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. ...
Applications
References
Back-propagation artificial neural networks
[127]
Bayesian networks
[128]
Bayesian confidence propagation neural networks
[129]
Feed forward neural networks
[130]
Flow ...
doi:10.1080/13102818.2017.1364977
fatcat:qmbiss53wfggtc7ayj2ysgt5rq
Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers
2010
Healthcare Informatics Research
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Then, in the first stage of classification task, we used 5 classification techniques such as decision tree, artificial neural networks, logistic regression, Bayesian networks, and Naïve Bayes to build ...
[22] analyzed protein sequence and classify proteins into the folds. That is, they extracted sequential patterns of proteins, which were then used to classify the unknown proteins. Chiang et al. ...
Conflict of Interest No potential conflict of interest relevant to this article was reported. ...
doi:10.4258/hir.2010.16.2.67
pmid:21818426
pmcid:PMC3089866
fatcat:prnnqu3zl5dthmrse5tez3mj3u
The Development and Application of Bayesian Networks Used in Data Mining Under Big Data
2018
DEStech Transactions on Social Science Education and Human Science
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Therefore, this paper starts with the Bayesian networks, stretching out the Bayesian formula, Bayesian network technology, as well as the advantages of the combination of data mining and the Bayesian networks ...
It also has great applications in the fields of the Internet, finance, natural language, biology, among which the Bayesian network has experienced significant development last year and has become a kind ...
of lower nodes and branches. (3) Neural network method: neural network attempts to build the neural structure of human brain. ...
doi:10.12783/dtssehs/adess2017/17888
fatcat:cwkmc5glrjbdxolpiac6gf4rke
An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs
[article]
2018
bioRxiv
pre-print
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We use our network to predict recombination hotspots from sequence, and identify high-resolution binding motifs for the recombination- initiation protein PRDM9, which were recently validated by high-resolution ...
Results: Here we show how to combine equivariant networks, a general mathematical framework for handling exact symmetries in CNNs, with Bayesian dropout, a version of MC dropout suggested by a reinterpretation ...
To improve on this situation, we showed how to combine equivariant neural networks (here, neural networks that exhibit exact reverse-complement symmetry) with Bayesian dropout. ...
doi:10.1101/351254
fatcat:xm2ic6zbjfctrmzzril4uku25q
An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs
2018
Bioinformatics
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We use our network to predict recombination hotspots from sequence, and identify binding motifs for the recombination-initiation protein PRDM9 previously unobserved in this data, which were recently validated ...
Convolutional neural networks (CNNs) have been tremendously successful in many contexts, particularly where training data are abundant and signal-to-noise ratios are large. ...
Conflict of Interest: none declared. ...
doi:10.1093/bioinformatics/bty964
pmid:30481258
pmcid:PMC6596897
fatcat:qbiuy4toqrgxvaxbxfq2sffhfu
Comparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification
2008
Journal of Biomedical Informatics
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Computational identification of missing enzymes plays a significant role in accurate and complete reconstruction of metabolic network for both newly sequenced and well-studied organisms. ...
Robust full Bayesian learning for radial basis networks 2001;13:2359-407.]) is adopted to estimate the model order and the parameters. ...
They would like to thank other members of the Life Science Imaging Group of the Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair (HCNR) and Brigham and Women's Hospital, HMS for ...
doi:10.1016/j.jbi.2007.09.002
pmid:17950040
fatcat:clzp2ezn3jgh3pr3x5plmq5jna
Predicting solvent accessibility: Higher accuracy using Bayesian statistics and optimized residue substitution classes
1996
Proteins: Structure, Function, and Bioinformatics
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These results demonstrate the applicability of this relatively simple Bayesian approach to structure prediction and illustrate the utility of the classification methodology previously developed to extract ...
We introduce a novel Bayesian probabilistic method for predicting the solvent accessibilities of amino acid residues in globular proteins. ...
We extend a general thanks to those who solve protein structures and make this information available, and to those who construct and maintain databases of protein sequences and structures. ...
doi:10.1002/(sici)1097-0134(199605)25:1<38::aid-prot4>3.3.co;2-h
pmid:8727318
fatcat:grlqntgl3nfnrjdsxjylkn33o4
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