| Autor: |
Dehghanzadeh H; Department of Animal Science Research, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran., Ghaderi-Zefrehei M; Department of Animal Science, Faculty of Agriculture, University of Yasouj, P. O. Box: 75914, Yasouj, Iran. mosmos741@gmail.com., Mirhoseini SZ; Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran., Esmaeilkhaniyan S; Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran., Haruna IL; Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand., Amirpour Najafabadi H; Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of applied genetics [J Appl Genet] 2020 May; Vol. 61 (2), pp. 231-238. Date of Electronic Publication: 2020 Jan 24. |
| DOI: |
10.1007/s13353-020-00543-x |
| Abstrakt: |
Information theory is a branch of mathematics that overlaps with communications, biology, and medical engineering. Entropy is a measure of uncertainty in the set of information. In this study, for each gene and its exons sets, the entropy was calculated in orders one to four. Based on the relative entropy of genes and exons, Kullback-Leibler divergence was calculated. After obtaining the Kullback-Leibler distance for genes and exons sets, the results were entered as input into 7 clustering algorithms: single, complete, average, weighted, centroid, median, and K-means. To aggregate the results of clustering, the AdaBoost algorithm was used. Finally, the results of the AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. All calculations were performed using the MATLAB Engineering Software (2015). Following our findings on investigating the results of genes metabolic pathways based on the gene annotations, it was revealed that our proposed clustering method yielded correct, logical, and fast results. This method at the same that had not had the disadvantages of aligning allowed the genes with actual length and content to be considered and also did not require high memory for large-length sequences. We believe that the performance of the proposed method could be used with other competitive gene clustering methods to group biologically relevant set of genes. Also, the proposed method can be seen as a predictive method for those genes bearing up weak genomic annotations. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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