DNA和蛋白质序列中词模式的重要性抽样
Importance Sampling of Word Patterns in DNA and Protein Sequences
摘要：
蒙特卡罗方法可以提供精确的p值估计的字计数检验统计量，并易于实现。他们是特别有吸引力的渐近理论是缺席时，无论是搜索序列或字模式是太短的渐近公式的应用。朴素的直接蒙特卡罗法不适用于小概率的估计，因为很少产生相关的罕见事件。相反，我们提出了有效的重要性抽样算法，使用控制插入所需的字模式随机生成的序列。实施说明生物学兴趣的单词模式：回文和反向重复，模式产生的位置特定的权重矩阵和共同出现的图案对。
英文摘要：
Monte Carlo methods can provide accurate p-value estimates of word counting test statistics and are easy to implement. They are especially attractive when an asymptotic theory is absent or when either the search sequence or the word pattern is too short for the application of asymptotic formulae. Naive direct Monte Carlo is undesirable for the estimation of small probabilities because the associated rare events of interest are seldom generated. We propose instead efficient importance sampling algorithms that use controlled insertion of the desired word patterns on randomly generated sequences. The implementation is illustrated on word patterns of biological interest: Palindromes and inverted repeats, patterns arising from position specific weight matrices and co-occurrences of pairs of motifs.
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