Abstract—Background is the numerous attempts were made to predict motifs in genomic sequences that correspond to poly (A) tail signals. Vast portion of this effort has been directed to a plethora of nonlinear classification methods. Even when such approaches yield good discriminant results, identifying dominant features of regulatory mechanisms nevertheless remains a challenge. In this work, we look at decision rules that may help identifying such features. Findings are we present a simple decision rule for classification of candidate poly (A) tail signal motifs in human genomic sequence obtained by evaluating features during the construction of gradient boosted trees. We found that values of a single feature based on the frequency of adenine in the genomic sequence surrounding candidate signal and the number of consecutive adenine molecules in a well-defined region immediately following the motif displays good discriminative potential in classification of poly (A) tail motifs for samples covered by the rule. Conclusions is the resulting simple rule can be used as an efficient filter in construction of more complex poly(A) tail motifs classification algorithms.

Index Terms—poly (A) tails, decision rules, genomic sequences, machine learning, classification.

Cite: Hassan Abou Eisha, Igor Chikalov, Mikhail Moshkov, and Boris Jankovic, "A Simple Decision Rule for Recognition of Poly(A) Tail Signal Motifs in Human Genome," Vol. 6, No. 2, pp. 71-74, May, 2015. doi:10.12720/jait.6.2.71-74