MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia.
School of Basic and Biomedical Science, Universidad Simón Bolívar, Barranquilla 080002, Colombia.
Molecules. 2020 Dec 23;26(1):20. doi: 10.3390/molecules26010020.
Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.
实时逆转录（RT）PCR 是检测严重急性呼吸系统综合症冠状病毒 2（SARS-CoV-2）的金标准，因为其具有灵敏度和特异性，从而满足了不断增加的病例需求。用于分析 PCR 结果的训练有素的分子生物学家的稀缺性使得数据验证成为一个挑战。人工智能（AI）旨在通过检测由于污染或伪影引起的 PCR 曲线中的非典型谱来缓解验证。生成了四类模拟实时 RT-PCR 曲线，即阳性、早期、无和异常扩增。使用每个类别的少量数据生成和测试机器学习（ML）模型。使用西蒙·玻利瓦尔大学病毒学实验室从 SARS-CoV-2 的实时 RT-PCR 曲线获得的大数据来对最佳模型进行分类，并对该模型进行重新训练并在软件中实现，该软件将患者数据与测试和 AI 诊断相关联。AI 的最佳策略包括从模拟数据生成的二进制分类模型，其中通过对第一个模型分析的数据进行分类，要么是阳性或阴性和异常。为了区分阴性和异常，使用第二个模型重新评估数据。在第一个模型中，数据需要通过预处理的组合进行预分析。早期扩增类被从模型中删除，因为大数据中的病例数量可以忽略不计。可以使用最小可用信息从模拟数据创建 ML 模型。在分析过程中，可以通过生成模拟数据来合并更改或变化，从而避免合并包含所有可能变化的大量实验数据。对于诊断 SARS-CoV-2，这种类型的 AI 对于优化 PCR 测试至关重要，因为它可以实现快速诊断并减少假阳性。我们的方法还可用于其他类型的分子分析。
