Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression

机译：使用支持向量回归法预测啤酒酵母14-3-3和GYF肽识别域的结合亲和力

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Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide binding affinity prediction models are proposed based on support vector regression. Proposed models are applied to yeast bmh 14-3-3 and syh GYF peptide-recognition domains. The cross validated results of the domain-peptide binding affinity data sets show that predictive performance of the support vector based models are efficient.

机译：蛋白质与其他蛋白质和生物分子相互作用，以在细胞中进行生物过程。计算模型有助于理解整个细胞生命周期中发生的复杂生化过程。域介导的蛋白质与肽的相互作用是生物信息学中这种复杂问题之一，需要计算预测模型来识别有意义的结合。在这项研究中，基于支持向量回归提出了域-肽结合亲和力预测模型。拟议的模型应用于酵母bmh 14-3-3和syh GYF肽识别域。域-肽结合亲和力数据集的交叉验证结果表明，基于支持向量的模型的预测性能是有效的。

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《Annual International Conference of the IEEE Engineering in Medicine and Biology Society》|2016年|3445-3448|共4页
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Volkan Uslan; Huseyin Seker;
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Peptides; Proteins; Support vector machines; Predictive models; Amino acids; Biological system modeling; Computational modeling;

机译：肽;蛋白质;支持向量机;预测模型;氨基酸;生物系统建模;计算建模;

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专利

1. Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression [J] . Uslan Volkan, Seker Huseyin Applied Soft Computing . 2016,第Null期

机译：混合模糊支持向量回归定量预测肽结合亲和力
2. Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression. [J] . Yuan Y, Zhang R, Hu R, European Journal of Medicinal Chemistry: Chimie Therapeutique . 2009,第1期

机译：基于启发式方法，支持向量机和投影寻踪回归，预测取代的1-（3,3-二苯丙基）-哌啶基酰胺和脲的CCR5受体结合亲和力。
3. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models [J] . Wen Liu, Xiangshan Meng, Qiqi Xu, BMC Bioinformatics . 2006,第1期

机译：使用支持向量机回归（SVR）模型定量预测I类MHC肽的小鼠结合亲和力
4. Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression [C] . Volkan Uslan, Huseyin Seker Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2016

机译：使用支持向量回归的S.Cerevisiae 14-3-3和Gyf肽识别域的结合亲和力预测
5. Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models [D] . Al-Ghraibah, Amani 2015

机译：支持向量回归的特征选择，火炬大小和火炬发射时间预测，以及基于时空测度的隐马尔可夫模型自动预测火炬行为
6. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models [O] . Wen Liu, Xiangshan Meng, Qiqi Xu, 2006

机译：使用支持向量机回归（SVR）模型对小鼠I类MHC肽结合亲和力的定量预测
7. Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression [O] . Uslan, Volkan, Seker, Huseyin 2016

机译：混合模糊支持向量回归定量预测肽结合亲和力

Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression

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