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，人们围绕提高跨膜片段预测的准确性展开了大量研究，并取得了许多有意义的成果。尽管有这些可观的结果，现有的方法缺乏解释学习结果如何达到以及为什么做出预测决策的过程的能力。对所做决策的解释对于机器学习技术在生物信息学应用中的接受是重要的，例如蛋白质结构预测。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都表现出很强的泛化能力，但它们是黑箱模型，难以理解。在我们目前的NSF项目（CCR 0514750），粗糙集数据分析已被提出的数据挖掘。在这个项目中，我们建议将我们的结果从目前的NSF项目数据挖掘生物信息学。特别是，我们建议使用一种创新的方法来理解transmembranesegments预测的规则生成集成粗糙集理论，支持向量机和基于关联规则的分类器的优点。我们相信，新的方法不仅可以用于跨膜片段的预测，但也为理解预测。预测和它的解释，可以用来指导生物实验。智能优点：这个建议的重点是结合联合收割机粗糙集理论，从我们目前的NSF项目，支持向量机和关联规则为基础的分类器，阐明一个新的跨膜段预测和理解的方法。虽然存在用于相同目的的几种方法，但该提案寻求在准确性和生成的模式的数量方面实现更好的性能。人们希望生成的模式可以很容易地理解和生物学意义，并可以被生物学家用来指导他们的实验。这种协作方法利用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。由此产生的软件将不仅能够预测跨膜片段，而且预测是如何实现的。更广泛的影响：虽然这个建议的重点是跨膜片段的预测和理解，但必须强调的是，新的方法和开发的软件工具是完全通用的，可以应用于其他领域，如蛋白质二级结构预测。合作者认为，本提案中描述的过程与最终产品一样重要，因为提案本身就是协作和跨学科的。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难题的现代研究方法的一部分，而且还使学生和研究人员接触到我们各自领域的前沿研究和问题。