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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective Teaching Learning Based Optimization Algorithm
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Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective Teaching Learning Based Optimization Algorithm

机译：基于混合多目标教学学习的优化算法在蛋白质序列中寻找模式

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摘要

Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.
机译：蛋白质是形成众生的分子。这些蛋白质以氨基酸等解离形式存在，并具有多种生物学功能，实际上，几乎所有人体反应都是在蛋白质的参与下发生的。这是蛋白质分析已成为生物学主要问题的原因之一。以更具体的方式，在一组相关蛋白质序列中的保守模式的鉴定可以提供有关这些蛋白质功能的相关生物学信息。在本文中，我们提出了一种新的算法，该算法基于基于教学学习的优化（TLBO）与专门用于预测蛋白质序列集中常见模式的局部搜索功能相结合。这种基于人群的进化算法定义了一组个体（解决方案），它们通过不同的学习阶段来增强其知识（质量）。因此，如果我们将其正确地适应所提到问题的生物学环境，我们将获得一套可接受的质量解决方案。为了评估所提出技术的性能，我们使用了六个实例，这些实例由从PROSITE数据库获得的不同相关蛋白质序列组成。就像我们将看到的那样，设计的方法可以很好地预测并提高其他知名生物学工具发现的解决方案的质量。

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