摘要翻译：
动机：轮廓隐马尔可夫模型（pHMMs）是一个流行和非常有用的工具，在检测远程同源蛋白家族。不幸的是，当蛋白质处于“黄昏区”时，它们的表现并不总是令人满意的。本文提出了HMMER-STRUCT模型构造算法和工具，它在训练pHMM的同时利用结构信息来提高pHMM的性能。作为第一步，HMMER-STRUCT构建一组PHMM。每个pHMM是通过根据残基的特定结构性质对排列的蛋白质中的每个残基进行加权来构建的。所使用的特性包括一级、二级和三级结构、可达性和包装。然后HMMER-STRUCT通过投票对结果进行优先级排序。结果：我们使用SCOP数据库进行实验。在整个过程中，我们对蛋白质超家族应用了保留一个家族的交叉验证。首先，我们使用MAMMOTH-mult结构对齐器对齐训练集的蛋白质。然后，我们进行了两组实验。在第一个实验中，我们比较了结构加权模型与标准pHMMs之间的差异。在第二个实验中，我们将投票模型与单个PHMM进行了比较。通过ROC曲线和查准率/查全率曲线比较方法的性能，并通过配对双尾t检验评价方法的显著性。我们的结果表明，所有结构加权模型都比默认HMMER有显著的性能改善，并且组合模型比原始模型和结构加权模型的灵敏度都有显著的改善。
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英文标题：
《A study of structural properties on profiles HMMs》
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作者：
Juliana S Bernardes, Alberto Davila, Vitor Santos Costa, Gerson
  Zaverucha
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Motivation: Profile hidden Markov Models (pHMMs) are a popular and very useful tool in the detection of the remote homologue protein families. Unfortunately, their performance is not always satisfactory when proteins are in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm and tool that tries to improve pHMM performance by using structural information while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs. Each pHMM is constructed by weighting each residue in an aligned protein according to a specific structural property of the residue. Properties used were primary, secondary and tertiary structures, accessibility and packing. HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP database to perform our experiments. Throughout, we apply leave-one-family-out cross-validation over protein superfamilies. First, we used the MAMMOTH-mult structural aligner to align the training set proteins. Then, we performed two sets of experiments. In a first experiment, we compared structure weighted models against standard pHMMs and against each other. In a second experiment, we compared the voting model against individual pHMMs. We compare method performance through ROC curves and through Precision/Recall curves, and assess significance through the paired two tailed t-test. Our results show significant performance improvements of all structurally weighted models over default HMMER, and a significant improvement in sensitivity of the combined models over both the original model and the structurally weighted models.
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PDF链接：
https://arxiv.org/pdf/0704.2010