蛋白质亚细胞定位的预测方法综述

A. A. Parikesit, Gabriella Patricia, Nanda Rizqia Pradana Ratnasari
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引用次数: 0

摘要

蛋白质亚细胞定位(SCL)的预测一直是生物信息学领域的一个长期挑战。蛋白质SCL对蛋白质正常行使其功能至关重要。蛋白质定位依赖于信号肽和基因本体(GO)数据库中可用的信息,这使得使用计算方法预测蛋白质SCL成为可能。SCL方法可以分为基于序列的方法和基于注释的方法。机器学习算法和分类器用于蛋白质SCL预测工具。本文综述了近5年来发表的蛋白质SCL预测因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction Methods of the Protein Subcellular Localization: A Systematic Reviews
The prediction of protein subcellular localization (SCL) has been a long-running challenge in bioinformatics. Protein SCL is crucial for a protein to exercise its functions properly. The reliance of protein localization on signaling peptides and the information available in gene ontology (GO) databases makes it possible to use computational approaches to predict protein SCL. SCL methods can be classified as either sequence-based or annotation-based. Machine learning algorithms and classifiers are used in protein SCL prediction tools. This review presents a list of protein SCL predictors published in the last 5 years.
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