基于Word2vec神经模型的抗COVID-19蛋白载体生成技术:机器学习方法

Toby A Adjuik, Daniel Ananey-Obiri
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引用次数: 11

摘要

2019年,COVID-19病毒袭击了世界,影响了全世界人民的健康、经济和生活方式。对付这一公共卫生问题的一种方法是使用适当、快速和公正的诊断工具,以便快速发现受感染者。然而,目前缺乏生物信息学工具,因此需要建模研究来帮助诊断COVID-19病例。实时逆转录聚合酶链反应(rRT-PCR)等基于分子的方法检测COVID-19耗时且容易受到污染。现代生物信息学工具使得建立各种疾病蛋白质序列的大型数据库、应用数据挖掘技术和准确诊断疾病成为可能。然而,目前使用这些数据库的序列比对工具由于序列高度不相似而无法检测新型COVID-19病毒序列。因此,本研究的目的是利用神经词嵌入技术生成的蛋白质载体,开发能够快速准确分类COVID-19病毒序列的模型。五种机器学习模型;K最近邻回归(KNN)、支持向量机(SVM)、随机森林(RF)、线性判别分析(LDA)和Logistic回归使用了国家生物技术中心的数据集。我们的结果表明,RF模型在训练数据集上的准确率为99%,在测试数据集上的准确率为99.5%,优于所有其他模型。这项研究的意义是,在疑似病例中快速检测COVID-19病毒可能会挽救生命,因为确定患者状态所需的时间更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach.

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach.

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach.

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach.

The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases. Molecular-based methods such as the real-time reverse transcription polymerase chain reaction (rRT-PCR) for detecting COVID-19 is time consuming and prone to contamination. Modern bioinformatics tools have made it possible to create large databases of protein sequences of various diseases, apply data mining techniques, and accurately diagnose diseases. However, the current sequence alignment tools that use these databases are not able to detect novel COVID-19 viral sequences due to high sequence dissimilarity. The objective of this study, therefore, was to develop models that can accurately classify COVID-19 viral sequences rapidly using protein vectors generated by neural word embedding technique. Five machine learning models; K nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), Linear discriminant analysis (LDA), and Logistic regression were developed using datasets from the National Center for Biotechnology. Our results suggest, the RF model performed better than all other models on the training dataset with 99% accuracy score and 99.5% accuracy on the testing dataset. The implication of this study is that, rapid detection of the COVID-19 virus in suspected cases could potentially save lives as less time will be needed to ascertain the status of a patient.

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