Size constrained clustering has been recently proposed to embed "a priori" knowledge in clustering methods. By exploiting the "string property" we propose an exact and efficient algorithm based on dynamic programming techniques to solve size-constrained one-dimensional clustering problems. We show the applicability of the proposed method in a difficult computational biology problem: the prediction of the transcription start sites of genes. The obtained experimental results clearly show the potential of the proposed approach when compared with previously published methods.
Identification of promoter regions in genomic sequences by 1-dimensional constraint clustering / A. Bertoni, M. Re, F. Saccà, G. Valentini - In: Neural nets, WIRN'11 : proceedings of the 21st Italian workshop on neural nets / [a cura di] B. Apolloni, S. Bassis, A. Esposito, C.F. Morabito. - Amsterdam : IOS press, 2011. - ISBN 9781607509714. - pp. 162-169 (( Intervento presentato al 21. convegno Neural Nets (WIRN) tenutosi a Vietri sul Mare nel 2011 [10.3233/978-1-60750-972-1-162].
Identification of promoter regions in genomic sequences by 1-dimensional constraint clustering
A. BertoniPrimo
;M. ReSecondo
;F. SaccàPenultimo
;G. ValentiniUltimo
2011
Abstract
Size constrained clustering has been recently proposed to embed "a priori" knowledge in clustering methods. By exploiting the "string property" we propose an exact and efficient algorithm based on dynamic programming techniques to solve size-constrained one-dimensional clustering problems. We show the applicability of the proposed method in a difficult computational biology problem: the prediction of the transcription start sites of genes. The obtained experimental results clearly show the potential of the proposed approach when compared with previously published methods.
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