Transmembrane Protein Segment Prediction and Understanding based on Machine Learning Methods

近年来，人们对提高跨膜片段预测的准确性进行了大量的研究，并取得了许多有意义的成果。尽管取得了这些可观的成果，但现有的方法缺乏解释学习结果如何达成以及为什么做出预测决策的过程的能力。一个决定的解释对于机器学习技术在生物信息学应用（如蛋白质结构预测）中的应用是很重要的。虽然支持向量机（SVM）在蛋白质结构预测等多个应用领域显示出较强的泛化能力，但其是黑箱模型，难以理解。在我们目前的NSF项目（CCR 0514750）中，粗糙集数据分析已经被提出用于数据挖掘。在这个项目中，我们建议将我们在数据挖掘方面的成果从目前的NSF项目扩展到生物信息学。特别是，我们建议使用一种创新的方法来生成规则，通过整合粗糙集理论、支持向量机和基于关联规则的分类器的优点来理解跨膜片段的预测。我们相信这种新方法不仅可以用于跨膜片段预测，而且可以用于理解预测。所得预测结果及其解释可用于指导生物学实验。智力优势：本提案的重点是将我们当前NSF项目的粗糙集理论，支持向量机和基于关联规则的分类器相结合，阐明一种跨膜片段预测及其理解的新方法。虽然存在几种用于相同目的的方法，但该建议寻求在准确性和生成模式的数量方面实现更好的性能。希望生成的模式可以很容易理解并具有生物学意义，可以被生物学家用来指导他们的实验。这种合作方法利用了PI在机器学习方面的优势和co-PI在跨膜方面的专业知识来验证我们的新方法。由此产生的软件不仅能够预测跨膜片段，而且能够预测如何实现。更广泛的影响：虽然本建议的重点是跨膜片段预测及其理解，但必须强调的是，新方法和开发的软件工具是完全可推广的，可以应用于其他领域，如蛋白质二级结构预测。合作者认为，该提案中描述的过程与最终产品一样重要，因为该提案本质上是协作和跨学科的。因此，这一提议立即成为增加计算机科学家和生物学家之间互动的一个跳跃点，它不仅是解决细胞生物学和复杂系统难题的现代研究方法的一部分，而且是让学生和研究人员接触到我们各自领域的前沿研究和问题的重要组成部分。

In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of a decision made is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. While support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction, they are black box models and hard to understand. In our current NSF project (CCR 0514750), rough sets data analysis has been proposed for data mining. In this project, we propose to extend our results in data mining from the current NSF project to bioinformatics. In particular, we propose to use an innovative approach to rule generation for understanding prediction of transmembranesegments by integrating the merits of rough set theory, SVMs and association rule based classifiers. We believe that the new approach can be used not only for transmembrane segments prediction, but also for understanding the prediction. The prediction and its interpretation obtained can be used for guiding biological experiments.Intellectual Merits: The focus of this proposal is to combine rough set theory from our current NSF project, SVMs and association rule based classifiers to elucidate a new approach for transmembrane segments prediction and its understanding. While there exist several methods for the same purpose, the proposal seeks to achieve better performance with respect to accuracy and the number of generated patterns. It is hoped that the patterns generated can be easily understandable and biologically meaningful and can be used by biologists to guide their experiments. This collaborative approach draws upon the strengths of the PI in machine learning and the co-PI's expertise in transmembrane to validate our new method. The resulting softwarewill not only be able to predict transmembrane segments, but moreover how the prediction is achieved.Broader Impacts: While the focus of the this proposal is on transmembrane segments prediction and its understanding, it must be emphasized that the new approach and the software tools developed are completely generalizable and can be applied to other domains such as protein secondary structure prediction. The collaborators believe the process described in this proposal is as important as the end product as the proposal is inherently collaborative and cross-disciplinary. As such, the proposal lends itself immediately as a jumping point for increasing the interaction between computer scientists and biologists, important not only as part of modern research approaches to tackling difficult problems in cell biology and complex systems but in exposing students and researchers to both the cutting edge research and problems that are manifest in eachof our respective fields.