Publications
2020 |
Rodríguez-Baena, D.; Gómez-Vela, F.; García-Torres, M.; Divina, F.; Barranco, C. D.; Díaz-Díaz, N.; Jiménez, M.; Montalvo, G. Identifying livestock behavior patterns based on accelerometer dataset Journal Article In: Journal of Computational Science, vol. 41, pp. 101076, 2020, ISSN: 1877-7503. Abstract | Links | BibTeX | Tags: Livestock activity, Pattern recognition, Time series processing @article{Rodríguez-Baena2020,In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed. In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming. |
2009 |
Alves, R.; Rodríguez-Baena, D.; Aguilar-Ruiz, J. Gene association analysis: a survey of frequent pattern mining from gene expression data Journal Article In: Briefings in Bioinformatics, vol. 11, no. 2, pp. 210-224, 2009, ISSN: 1467-5463. Abstract | Links | BibTeX | Tags: Gene expression analysis, Pattern recognition @article{Alves2009,Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable. |