Katuwawala Akila, Ghadermarzi Sina, Hu Gang, Wu Zhonghua, Kurgan Lukasz
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.
School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China.
Comput Struct Biotechnol J. 2021 Apr 27;19:2597-2606. doi: 10.1016/j.csbj.2021.04.066. eCollection 2021.
A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus's predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTERplus/.
疾病预测领域的一项最新进展是质量评估（QA）分数的发展。QA分数通过识别这些预测更可能正确的区域，对疾病预测器产生的倾向进行补充。我们开发、实证测试并发布了一种新的QA工具QUARTERplus，它解决了当前QA方法QUARTER的几个关键缺点。QUARTERplus是第一个利用QA分数和相关的输入疾病预测，借助现代深度学习元模型来产生非常准确的疾病预测的解决方案。深度神经网络利用QA分数来识别和修正原始/输入疾病预测较差的区域。更重要的是，准确的QUARTERplus预测伴随着易于解释的残基水平QA分数，这些分数可靠地量化了它们的残基水平预测质量。我们为QUARTERplus和其他10种流行的疾病预测器提供了这些可解释的QA分数。在一个大型且独立（低相似度）的测试数据集上的实证测试表明，QUARTERplus预测的AUC = 0.93，并且在统计上比12种最先进的疾病预测器的结果更准确。我们还证明，QUARTERplus产生的新QA分数与实际预测质量高度相关，并且它们可以有效地用于识别正确疾病预测的区域。这一特性使用户能够轻松识别现代疾病预测器生成的预测中哪些部分更值得信赖。QUARTERplus可通过http://biomine.cs.vcu.edu/servers/QUARTERplus/这个便捷的网络服务器获取。
