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Entropy 2015, 17(4), 1673-1689; doi:10.3390/e17041673

Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction

1
Escuela de Ciencias de la Computación y de la Informática, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, Código Postal 2060-San José, Costa Rica
2
Escuela de Ingeniería Eléctrica, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, Código Postal 2060-San José, Costa Rica
This paper is an extended version of our paper published in Proceedings of International Work Conference on Bio-inspired Intelligence (IWOBI), Costa Rica, 16–18 July 2014; pp. 71–75, doi:10.1109/IWOBI.2014.6913941.
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos M. Travieso-González and Jesús B. Alonso-Hernández
Received: 27 November 2014 / Revised: 19 February 2015 / Accepted: 17 March 2015 / Published: 27 March 2015
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
View Full-Text   |   Download PDF [369 KB, uploaded 27 March 2015]   |  

Abstract

This study delves further into the analysis of genomic data by computing a variety of complexity measures. We analyze the effect of window size and evaluate the precision and recall of the prediction of gene zones, aided with a much larger dataset (full chromosomes). A technique based on the separation of two cases (gene-containing and non-gene-containing) has been developed as a basic gene predictor for automated DNA analysis. This predictor was tested on various sequences of human DNA obtained from public databases, in a set of three experiments. The first one covers window size and other parameters; the second one corresponds to an analysis of a full human chromosome (198 million nucleic acids); and the last one tests subject variability (with five different individual subjects). All three experiments have high-quality results, in terms of recall and precision, thus indicating the effectiveness of the predictor. View Full-Text
Keywords: information complexity; DNA; genomic variability; gene prediction; nucleic acid sequence information complexity; DNA; genomic variability; gene prediction; nucleic acid sequence
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Monge, R.E.; Crespo, J.L. Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction. Entropy 2015, 17, 1673-1689.

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