Transmembrane Protein Segment Prediction and Understanding based on Machine Learning Methods

基于机器学习方法的跨膜蛋白片段预测与理解

• 批准号: 0646102
• 负责人: Yi Pan
• 金额: --
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0646102&HistoricalAwards=false
• 关键词: Transmembrane; Protein; Segment; Prediction; Understanding

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.

近年来，围绕提高跨膜片段预测的准确性开展了许多研究，并取得了许多有意义的成果。尽管取得了这些可观的结果，但现有的方法缺乏解释学习结果如何达到的过程以及为什么做出预测决策的能力。对决策的解释对于机器学习技术在生物信息学应用（例如蛋白质结构预测）中的接受非常重要。虽然支持向量机（SVM）在包括蛋白质结构预测在内的许多应用领域表现出了强大的泛化能力，但它们是黑盒模型并且难以理解。在我们当前的 NSF 项目 (CCR 0514750) 中，已提议将粗糙集数据分析用于数据挖掘。在这个项目中，我们建议将数据挖掘的成果从当前的 NSF 项目扩展到生物信息学。特别是，我们建议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，使用创新的规则生成方法来理解跨膜片段的预测。我们相信，新方法不仅可以用于跨膜片段预测，还可以用于理解预测。获得的预测及其解释可用于指导生物实验。 智力优点：该提案的重点是将我们当前 NSF 项目中的粗糙集理论、SVM 和基于关联规则的分类器相结合，以阐明跨膜片段预测和预测的新方法。它的理解。虽然存在用于同一目的的多种方法，但该提案寻求在准确性和生成模式的数量方面实现更好的性能。希望生成的模式易于理解且具有生物学意义，并且可以被生物学家用来指导他们的实验。这种协作方法利用了 PI 在机器学习方面的优势和联合 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，而且还能预测如何实现预测。 更广泛的影响：虽然该提案的重点是跨膜片段预测及其理解，但必须强调的是，新方法和软件开发的工具是完全通用的，可以应用于其他领域，例如蛋白质二级结构预测。合作者认为，该提案中描述的流程与最终产品一样重要，因为该提案本质上是协作性和跨学科的。因此，该提案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，不仅作为解决细胞生物学和复杂系统中的难题的现代研究方法的一部分很重要，而且使学生和研究人员能够接触到计算机科学家和生物学家之间的互动。我们各自领域中存在的前沿研究和问题。