Base de dados : MEDLINE
Pesquisa : G17.035.250.500.500 [Categoria DeCS]
Referências encontradas : 149 [refinar]
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  1 / 149 MEDLINE  
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[PMID]:29017921
[Au] Autor:de Ávila MB; Xavier MM; Pintro VO; de Azevedo WF
[Ad] Endereço:Laboratory of Computational Systems Biology, School of Sciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil; Graduate Program in Cellular and Molecular Biology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av.
[Ti] Título:Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.
[So] Source:Biochem Biophys Res Commun;494(1-2):305-310, 2017 Dec 09.
[Is] ISSN:1090-2104
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores.
[Mh] Termos MeSH primário: Antineoplásicos/química
Quinase 2 Dependente de Ciclina/antagonistas & inibidores
Inibidores de Proteínas Quinases/química
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Quinase 2 Dependente de Ciclina/química
Bases de Dados de Proteínas
Conjuntos de Dados como Assunto
Desenho de Drogas
Seres Humanos
Concentração Inibidora 50
Ligantes
Simulação de Acoplamento Molecular
Curva ROC
Termodinâmica
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Antineoplastic Agents); 0 (Ligands); 0 (Protein Kinase Inhibitors); EC 2.7.11.22 (CDK2 protein, human); EC 2.7.11.22 (Cyclin-Dependent Kinase 2)
[Em] Mês de entrada:1711
[Cu] Atualização por classe:171108
[Lr] Data última revisão:
171108
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171012
[St] Status:MEDLINE


  2 / 149 MEDLINE  
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[PMID]:28922353
[Au] Autor:Villoutreix P; Andén J; Lim B; Lu H; Kevrekidis IG; Singer A; Shvartsman SY
[Ad] Endereço:Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.
[Ti] Título:Synthesizing developmental trajectories.
[So] Source:PLoS Comput Biol;13(9):e1005742, 2017 Sep.
[Is] ISSN:1553-7358
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics.
[Mh] Termos MeSH primário: Padronização Corporal/fisiologia
Processamento de Imagem Assistida por Computador/métodos
Modelos Biológicos
[Mh] Termos MeSH secundário: Animais
Biologia Computacional
Drosophila/crescimento & desenvolvimento
Microscopia Confocal
Aprendizado de Máquina Supervisionado
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171018
[Lr] Data última revisão:
171018
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170919
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pcbi.1005742


  3 / 149 MEDLINE  
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[PMID]:28732052
[Au] Autor:Jia L; Sun Y
[Ad] Endereço:Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, United States of America.
[Ti] Título:Protein asparagine deamidation prediction based on structures with machine learning methods.
[So] Source:PLoS One;12(7):e0181347, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.
[Mh] Termos MeSH primário: Asparagina/química
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Amidas/química
Simulação por Computador
Mineração de Dados
Modelos Estatísticos
Estrutura Molecular
Curva ROC
Software
Solventes/química
[Pt] Tipo de publicação:JOURNAL ARTICLE; VALIDATION STUDIES
[Nm] Nome de substância:
0 (Amides); 0 (Solvents); 7006-34-0 (Asparagine)
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171002
[Lr] Data última revisão:
171002
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170722
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0181347


  4 / 149 MEDLINE  
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[PMID]:28665601
[Au] Autor:Cordier T; Esling P; Lejzerowicz F; Visco J; Ouadahi A; Martins C; Cedhagen T; Pawlowski J
[Ad] Endereço:Department of Genetics and Evolution, University of Geneva , Boulevard d'Yvoy 4, CH 1205 Geneva, Switzerland.
[Ti] Título:Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.
[So] Source:Environ Sci Technol;51(16):9118-9126, 2017 Aug 15.
[Is] ISSN:1520-5851
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.
[Mh] Termos MeSH primário: Código de Barras de DNA Taxonômico
Foraminíferos
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Biodiversidade
Ecologia
Monitoramento Ambiental
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1711
[Cu] Atualização por classe:171106
[Lr] Data última revisão:
171106
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170701
[St] Status:MEDLINE
[do] DOI:10.1021/acs.est.7b01518


  5 / 149 MEDLINE  
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[PMID]:28659176
[Au] Autor:Kalatskaya I; Trinh QM; Spears M; McPherson JD; Bartlett JMS; Stein L
[Ad] Endereço:Informatics and Bio-computing, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. ikalats@gmail.com.
[Ti] Título:ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.
[So] Source:Genome Med;9(1):59, 2017 Jun 29.
[Is] ISSN:1756-994X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison. RESULTS: In this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues). CONCLUSIONS: In this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN .
[Mh] Termos MeSH primário: Análise Mutacional de DNA/métodos
Sequenciamento de Nucleotídeos em Larga Escala/métodos
Mutação
Neoplasias/genética
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Seres Humanos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170901
[Lr] Data última revisão:
170901
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170630
[St] Status:MEDLINE
[do] DOI:10.1186/s13073-017-0446-9


  6 / 149 MEDLINE  
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[PMID]:28641555
[Au] Autor:Heck GS; Pintro VO; Pereira RR; de Ávila MB; Levin NMB; de Azevedo WF
[Ad] Endereço:Laboratory of Computational Systems Biology, Faculty of Biosciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre-RS 90619-900. Brazil.
[Ti] Título:Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.
[So] Source:Curr Med Chem;24(23):2459-2470, 2017.
[Is] ISSN:1875-533X
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. OBJECTIVE: Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. METHOD: We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. RESULTS: Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. CONCLUSION: Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches.
[Mh] Termos MeSH primário: Ligantes
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Sítios de Ligação/efeitos dos fármacos
Seres Humanos
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Nm] Nome de substância:
0 (Ligands)
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170920
[Lr] Data última revisão:
170920
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170624
[St] Status:MEDLINE
[do] DOI:10.2174/0929867324666170623092503


  7 / 149 MEDLINE  
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[PMID]:28570593
[Au] Autor:Sverchkov Y; Craven M
[Ad] Endereço:Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
[Ti] Título:A review of active learning approaches to experimental design for uncovering biological networks.
[So] Source:PLoS Comput Biol;13(6):e1005466, 2017 Jun.
[Is] ISSN:1553-7358
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area.
[Mh] Termos MeSH primário: Biologia Computacional/métodos
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Algoritmos
Redes Reguladoras de Genes
Redes e Vias Metabólicas
Projetos de Pesquisa
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170627
[Lr] Data última revisão:
170627
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170602
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pcbi.1005466


  8 / 149 MEDLINE  
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[PMID]:28542398
[Au] Autor:Nishio M; Nakane K; Kubo T; Yakami M; Emoto Y; Nishio M; Togashi K
[Ad] Endereço:Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan.
[Ti] Título:Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.
[So] Source:PLoS One;12(5):e0178217, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:OBJECTIVE: The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ. MATERIALS AND METHODS: A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area (LAA%). The following values related to HEQ were obtained: nb0 and nb1. LAA% and HEQ were calculated at various threshold levels ranging from -1000 HU to -700 HU. Spearman's correlation coefficients between emphysema quantification and visual score were calculated at the various threshold levels. Visual score was predicted by machine learning and emphysema quantification (LAA% or HEQ). Random Forest was used as a machine learning algorithm, and accuracy of prediction was evaluated by leave-one-patient-out cross validation. The difference in the accuracy was assessed using McNemar's test. RESULTS: The correlation coefficients between emphysema quantification and visual score were as follows: LAA% (-950 HU), 0.567; LAA% (-910 HU), 0.654; LAA% (-875 HU), 0.704; nb0 (-950 HU), 0.552; nb0 (-910 HU), 0.629; nb0 (-875 HU), 0.473; nb1 (-950 HU), 0.149; nb1 (-910 HU), 0.519; and nb1 (-875 HU), 0.716. The accuracy of prediction was as follows: LAA%, 55.7% and HEQ, 66.1%. The difference in accuracy was statistically significant (p = 0.0290). CONCLUSION: LAA% and HEQ at -875 HU showed a stronger correlation with visual score than those at -910 or -950 HU. HEQ was more useful than LAA% for predicting visual score.
[Mh] Termos MeSH primário: Pulmão/diagnóstico por imagem
Enfisema Pulmonar/diagnóstico por imagem
Interpretação de Imagem Radiográfica Assistida por Computador/métodos
[Mh] Termos MeSH secundário: Seres Humanos
Doença Pulmonar Obstrutiva Crônica/diagnóstico por imagem
Reprodutibilidade dos Testes
Índice de Gravidade de Doença
Aprendizado de Máquina Supervisionado
Tomografia Computadorizada por Raios X
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170912
[Lr] Data última revisão:
170912
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170526
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0178217


  9 / 149 MEDLINE  
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[PMID]:28438725
[Au] Autor:Wang X; Zhao K; Street N
[Ad] Endereço:Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA, United States.
[Ti] Título:Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective.
[So] Source:J Med Internet Res;19(4):e130, 2017 Apr 24.
[Is] ISSN:1438-8871
[Cp] País de publicação:Canada
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. OBJECTIVE: This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. METHODS: We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. RESULTS: Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. CONCLUSIONS: Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.
[Mh] Termos MeSH primário: Serviços de Saúde/utilização
Internet/utilização
Participação do Paciente/estatística & dados numéricos
Apoio Social
[Mh] Termos MeSH secundário: Blogging/utilização
Mineração de Dados
Seres Humanos
Neoplasias/psicologia
Grupo Associado
Grupos de Autoajuda/utilização
Mídias Sociais/utilização
Aprendizado de Máquina Supervisionado
Análise de Sobrevida
Sobreviventes/psicologia
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171026
[Lr] Data última revisão:
171026
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170426
[St] Status:MEDLINE
[do] DOI:10.2196/jmir.6834


  10 / 149 MEDLINE  
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[PMID]:28361709
[Au] Autor:Ren J; Song J; Ellis J; Li J
[Ad] Endereço:Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
[Ti] Título:Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.
[So] Source:BMC Genomics;18(Suppl 2):113, 2017 03 14.
[Is] ISSN:1471-2164
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by diverse species, extra heterogeneity can also be introduced by various data sources, adding another layer of complexity and further confounding the research. RESULTS: This work proposed a staged heterogeneity learning method, which learns both characteristics and heterogeneity of data in a phased manner. The method was applied to identify antigenic residues of heterogenous conformational B-cell epitopes based on antigen sequences. In the first stage, the model learns the general epitope patterns of each kind of propensity from a large data set containing computationally defined epitopes. In the second stage, the model learns the heterogenous complementarity of these propensities from a relatively small guided data set containing experimentally determined epitopes. Moreover, we designed an algorithm to cluster the predicted individual antigenic residues into conformational B-cell epitopes so as to provide strong potential for real-world applications, such as vaccine development. With heterogeneity well learnt, the transferability of the prediction model was remarkably improved to handle new data with a high level of heterogeneity. The model has been tested on two data sets with experimentally determined epitopes, and on a data set with computationally defined epitopes. This proposed sequence-based method achieved outstanding performance - about twice that of existing methods, including the sequence-based predictor CBTOPE and three other structure-based predictors. CONCLUSIONS: The proposed method uses only antigen sequence information, and thus has much broader applications.
[Mh] Termos MeSH primário: Complexo Antígeno-Anticorpo/química
Antígenos/química
Epitopos de Linfócito B/química
Modelos Estatísticos
Aprendizado de Máquina Supervisionado
[Mh] Termos MeSH secundário: Complexo Antígeno-Anticorpo/imunologia
Antígenos/imunologia
Linfócitos B/química
Linfócitos B/imunologia
Sítios de Ligação
Mapeamento de Epitopos
Epitopos de Linfócito B/imunologia
Seres Humanos
Ligação Proteica
Conformação Proteica em alfa-Hélice
Conformação Proteica em Folha beta
Domínios e Motivos de Interação entre Proteínas
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Antigen-Antibody Complex); 0 (Antigens); 0 (Epitopes, B-Lymphocyte)
[Em] Mês de entrada:1709
[Cu] Atualização por classe:171120
[Lr] Data última revisão:
171120
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170401
[St] Status:MEDLINE
[do] DOI:10.1186/s12864-017-3493-0



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