arXiv 1310.43422013-10-16cs.AIcs.CE

基于元胞自动机的人工免疫系统在蛋白质预测中的应用

An Extensive Report on Cellular Automata Based Artificial Immune System for Strengthening Automated Protein Prediction

JNTU Hyderabad（贾瓦哈拉尔尼赫鲁科技大学海得拉巴校区）；Acharya Nagarjuna University（阿查里亚龙树大学）；JNTU Kakinada（贾瓦哈拉尔尼赫鲁科技大学卡基纳达校区）

Pokkuluri Kiran Sree, Inampudi Ramesh Babuhor, SSSN Usha Devi N3

备注：: arXiv admin note: text overlap with arXiv:0801.4312 by other authors
发表信息：: Advances in Biomedical Engineering Research (ABER) Volume 1 Issue 3, September 2013

摘要：

人工免疫系统（AIS-MACA）是一种新的计算智能技术，它可以增强自动蛋白质预测系统的适应性和并行性。大多数现有的方法是顺序的，将输入分为四个主要类别，这些是为类似的序列而设计的。AIS-MACA被设计为从与具有混合和混合变异的训练序列共享模糊区相似性和同一性的序列中识别10个类。该方法还预测了二级结构的三种状态（螺旋、链和卷曲）。我们的综合设计考虑了10个特征选择方法和4个分类器，以开发基于MACA（多吸引子元胞自动机）的分类器，为10个类中的每一个构建。我们已经用twilight-zone和1-high-similarity基准数据集测试了所提出的分类器，这些数据集具有超过30个现代竞争预测器，表明AIS-MACA提供了最佳的整体准确度，范围在80%和89.8%之间，具体取决于数据集。

英文摘要：

Artificial Immune System (AIS-MACA) a novel computational intelligence technique is can be used for strengthening the automated protein prediction system with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-MACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-MACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset.

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。