Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction†
AbstractThis 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
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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.
Monge RE, Crespo JL. Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction. Entropy. 2015; 17(4):1673-1689.Chicago/Turabian Style
Monge, Ricardo E.; Crespo, Juan L. 2015. "Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction." Entropy 17, no. 4: 1673-1689.