用隐马尔可夫模型发现转录因子结合位点的统计力学
Statistical mechanics of transcription-factor binding site discovery using Hidden Markov Models
摘要：
隐马尔可夫模型（Hidden Markov Models，HMF）是从DNA序列数据中推断转录因子（Transcription Factor，TF）结合位点的常用工具。我们利用TF结合的HSTH和一维无序势中硬棒的“逆”统计力学之间的数学等价性来研究HSTH中的学习。我们推导出解析表达式的Fisher信息，一个常用的措施，学习参数的置信度，在生物学相关的限制，结合位点的密度是低的。然后，我们使用统计力学的技术，推导出一个缩放原理，将TF的特异性（结合能）与学习它所需的最小训练数据量相关联。
英文摘要：
Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the "inverse" statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where the density of binding sites is low. We then use techniques from statistical mechanics to derive a scaling principle relating the specificity (binding energy) of a TF to the minimum amount of training data necessary to learn it.
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