Improving conditional random field model for prediction of protein-RNA residue-base contacts
Improving conditional random field model for prediction of protein-RNA residue-base contacts
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DOI：
10.1007/s40484-018-0136-7
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发表时间：
2018-06
影响因子：
3.1
通讯作者：
M. Hayashida;Noriyuki Okada;M. Kamada;H. Koyano
中科院分区：
文献类型：
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作者：
M. Hayashida;Noriyuki Okada;M. Kamada;H. Koyano
研究背景分析蛋白质残基与RNA碱基之间的相互作用对于理解生物细胞系统具有重要意义。提出了一种基于条件随机场（CRFs）的预测残基与碱基接触的方法，该方法分别接收给定蛋白质和RNA序列的多序列比对，通过最大化伪似然函数来学习包含多个参数的模型，这些参数涉及相邻残基-碱基对之间的关系。我们提出了一种新的基于CRF的模型，与以前的模型相比，随机变量之间的依赖关系更复杂，但为了避免对训练数据的过拟合，需要较少的参数。提出的模型，并取AUC（受试者工作特征曲线下面积）评分的平均值。结果表明，在CRFs.ConclusionsWe提出了一种新的随机模型来预测蛋白质-RNA残基-碱基接触，并提高了AUC评分的预测精度。这意味着在CRF中更多的依赖关系可以由更少的参数控制。
BackgroundFor understanding biological cellular systems, it is important to analyze interactions between protein residues and RNA bases. A method based on conditional random fields (CRFs) was developed for predicting contacts between residues and bases, which receives multiple sequence alignments for given protein and RNA sequences, respectively, and learns the model with many parameters involved in relationships between neighboring residue‐base pairs by maximizing the pseudo likelihood function.MethodsIn this paper, we proposed a novel CRF‐based model with more complicated dependency relationships between random variables than the previous model, but which takes less parameters for the sake of avoidance of overfitting to training data.ResultsWe performed cross‐validation experiments for evaluating the proposed model, and took the average of AUC (area under receiver operating characteristic curve) scores. The result suggests that the proposed CRF‐based model without usingL1‐norm regularization (lasso) outperforms the existing model with and without the lasso under several input observations to CRFs.ConclusionsWe proposed a novel stochastic model for predicting protein‐RNA residue‐base contacts, and improved the prediction accuracy in terms of the AUC score. It implies that more dependency relationships in a CRF could be controlled by less parameters.