TY - JOUR AU - Riley, Todd AU - Yu, Xin AU - Sontag, Eduardo AU - Levine, Arnold PY - 2009 DA - 2009/04/20 TI - The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes JO - BMC Bioinformatics SP - 111 VL - 10 IS - 1 AB - A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel "Corresponded Baum-Welch" training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding site's distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-10-111 DO - 10.1186/1471-2105-10-111 ID - Riley2009 ER -