NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
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DOI：
10.1093/bib/bbab167
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发表时间：
2021-05-11
影响因子：
9.5
通讯作者：
Kurata, Hiroyuki
中科院分区：
文献类型：
--
作者：
Hasan, Md Mehedi;Alam, Md Ashad;Kurata, Hiroyuki
神经肽（neuropeptides，NPs）是免疫系统中最通用的神经递质，调节各种中枢焦虑激素。免疫信息学是基础研究和药物开发不可或缺的一个重要组成部分，而高效、有效的生物信息学工具是快速、准确、大规模鉴定纳米粒的关键。虽然已经开发了一些NP预测工具，但提高NP的预测性能是必要的。在这项研究中，我们已经开发了一个基于机器学习的元预测称为NeuroPred-FRL采用的特征表示学习方法。首先，我们通过采用11种不同的编码，6种不同的分类器和两步特征选择方法生成了66个最佳基线模型。基于66个基线模型的NP的预测概率得分被组合以被视为输入特征向量。其次，为了增强特征表示能力，我们采用两步特征选择方法来优化66-D概率特征向量，然后将最优特征向量输入到随机森林分类器中，用于最终的元模型（NeuroPred-FRL）构建。基于交叉验证和独立检验的基准实验表明，与其他最先进的预测器相比，NeuroPred-FRL实现了NP的上级预测性能。我们相信，拟议的NeuroPred-FRL可以作为一个强大的工具，大规模识别的NP，促进其功能机制的表征，并加快其在临床治疗中的应用。此外，我们利用强大的SHapley加法解释算法解释了NeuroPred-FRL的一些模型机制。
Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive explanation algorithm.