Methods for Protein Subcellular Localization Prediction

Eric Y. T. Juan, J. Chang, C. H. Li, B. Y. Chen
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引用次数: 2

Abstract

Large-scale protein analysis and reliable annotations are particularly helpful for scholars in biology and medicine community. Understanding the functional characterizations of protein sequences has been a major challenge in recent years. Extensive computer based prediction systems have been developed to support the need since many proteins¡¦ sub cellular localizations are still unknown. In this work, numerous protein description methods and three common classifiers are used in our experiments. These protein description methods are classified into two categories: protein composition based and position-specific scoring matrix based. A better prediction is achieved upon widely used data sets. Through these experiments, it is expected to give a comparison between protein description methods for protein sub cellular localization and their classification characteristics.
蛋白质亚细胞定位预测方法
大规模的蛋白质分析和可靠的注释对生物学和医学界的学者特别有帮助。了解蛋白质序列的功能特征是近年来的主要挑战。由于许多蛋白质的亚细胞定位仍然是未知的,广泛的基于计算机的预测系统已经被开发出来以支持这一需求。在这项工作中,我们的实验中使用了多种蛋白质描述方法和三种常见的分类器。这些蛋白质描述方法分为两类:基于蛋白质组成和基于位置特异性评分矩阵。在广泛使用的数据集上实现更好的预测。通过这些实验,期望对蛋白质亚细胞定位的蛋白质描述方法及其分类特点进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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