TY - GEN
T1 - Pattern analysis in DNA microarray data through PCA-based gene selection
AU - Ocampo, Ricardo
AU - de Luna, Marco A.
AU - Vega, Roberto
AU - Sanchez-Ante, Gildardo
AU - Falcon-Morales, Luis E.
AU - Sossa, Humberto
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. In this paper, we propose a new methodology that is based on the application of Principal Component Analysis and other statistical tools to gain insight in the identification of relevant genes. We run the approaches using two benchmark datasets: Leukemia and Lymphoma. The results show that it is possible to reduce considerably the number of genes while increasing the performance of well known classifiers.
AB - DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. In this paper, we propose a new methodology that is based on the application of Principal Component Analysis and other statistical tools to gain insight in the identification of relevant genes. We run the approaches using two benchmark datasets: Leukemia and Lymphoma. The results show that it is possible to reduce considerably the number of genes while increasing the performance of well known classifiers.
UR - http://www.scopus.com/inward/record.url?scp=84949143589&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-12568-8_65
DO - 10.1007/978-3-319-12568-8_65
M3 - Contribución a la conferencia
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 532
EP - 539
BT - Progress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
A2 - Bayro-Corrochano, Eduardo
A2 - Hancock, Edwin
PB - Springer Verlag
T2 - 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
Y2 - 2 November 2014 through 5 November 2014
ER -