Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model?

Ivan Erjavac , Daniela Kalafatovic , Goran Mauša
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引用次数: 8

Abstract

Current application of machine learning in the process of antimicrobial peptide discovery call for the reduction of the false positive predictions that are produced by the classification models. Considering that the positive predictions of high confidence drive modern experimental design, the model’s sensitivity is crucial to reduce the number of unnecessary in vitro tests. Furthermore, taking into account the expert-based design approaches that employ random mutations on confirmed sequences, the machine learning models are required to distinguish between subtle differences among shuffled sequences. With the goal of reducing the false positive rate and improving sensitivity, we propose a hybrid approach to antimicrobial peptide prediction that utilizes combined encoding models. To this end, we implement models that employ both the physico-chemical features and sequence ordering information to stress the importance of using both representations. We also investigate the usage of binary encoding for peptide representation purposes, a method that is insufficiently represented in related research, which proved to act as a viable low dimensional alternative to the one-hot encoding. Our results, supported by Cochran and McNemar statistical tests and Spearman correlation analysis, indicate that the sequence-based encodings complement the physico-chemical features and their synergic effect yields improvement in terms of every evaluation metric. Finally, the proposed hybrid approach that combines physico-chemical features and binary encoding using logical conjunction was shown to be superior to other single models by a factor of 2.96 in terms of fall-out and up to 6.1% in terms of precision.

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抗菌肽预测的耦合编码方法:高度准确的模型有多敏感?
当前机器学习在抗菌肽发现过程中的应用要求减少由分类模型产生的假阳性预测。考虑到高置信度的积极预测驱动着现代实验设计,该模型的灵敏度对于减少不必要的体外试验数量至关重要。此外,考虑到基于专家的设计方法在已确认的序列上采用随机突变,机器学习模型需要区分洗牌序列之间的细微差异。为了降低假阳性率和提高敏感性,我们提出了一种利用组合编码模型进行抗菌肽预测的混合方法。为此,我们实现了同时使用物理化学特征和序列排序信息的模型,以强调使用这两种表示的重要性。我们还研究了用于肽表示目的的二进制编码的使用,这是一种在相关研究中没有充分代表的方法,它被证明是一种可行的低维替代单热编码。我们的研究结果得到了Cochran和McNemar统计测试和Spearman相关分析的支持,表明基于序列的编码补充了物理化学特征,它们的协同效应在每个评价指标方面都有所改善。最后,所提出的混合方法结合了物理化学特征和使用逻辑连接的二进制编码,在辐射系数方面优于其他单一模型2.96,在精度方面优于6.1%。
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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
发文量
0
审稿时长
15 days
期刊最新文献
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