一种自适应的G蛋白偶联受体分类策略
An Adaptive Strategy for the Classification of G-Protein Coupled Receptors
摘要：
计算生物学中的主要问题之一是现有的分类模型无法将扩展和新的领域知识结合起来。本文通过引入增量学习来解决生物信息学中静态分类模型的问题。许多机器学习工具已经被应用到这个问题，使用静态机器学习结构，如神经网络或支持向量机，不能适应新的信息到他们现有的模型。我们利用模糊ARTMAP作为替代机器学习系统，具有增量学习新数据的能力，因为它变得可用。模糊ARTMAP被发现是许多广泛的机器学习系统相媲美。使用的进化策略中的选择和组合成一个集成系统，再加上模糊ARTMAP的增量学习能力的个体分类器被证明是适合作为一个模式分类器。所提出的算法进行了测试，从G-偶联蛋白受体数据库的数据，并显示出良好的准确性为83%。该系统具有普遍适用性，可用于解决基因组学和蛋白质组学中的问题。
英文摘要：
One of the major problems in computational biology is the inability of existing classification models to incorporate expanding and new domain knowledge. This problem of static classification models is addressed in this paper by the introduction of incremental learning for problems in bioinformatics. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new information into their existing models. We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available. The fuzzy ARTMAP is found to be comparable to many of the widespread machine learning systems. The use of an evolutionary strategy in the selection and combination of individual classifiers into an ensemble system, coupled with the incremental learning ability of the fuzzy ARTMAP is proven to be suitable as a pattern classifier. The algorithm presented is tested using data from the G-Coupled Protein Receptors Database and shows good accuracy of 83%. The system presented is also generally applicable, and can be used in problems in genomics and proteomics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
