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ORCIDÁlvaro Mateos
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2000 – 2009
- 2004
[j4]Javier Herrero
, Juan M. Vaquerizas, Fátima Al-Shahrour, Lucía Conde, Álvaro Mateos, Javier Santoyo, Ramón Díaz-Uriarte, Joaquín Dopazo:
New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Res. 32(Web-Server-Issue): 485-491 (2004)- [c5]Javier Herrero, Juan M. Vaquerizas, Fátima Al-Shahrour, Lucía Conde, Álvaro Mateos, Javier Santoyo, Ramón Díaz-Uriarte, Joaquín Dopazo:
New Challenges in Gene Expression Data Analysis and the Extended GEPAS. Spanish Bioinformatics Conference 2004: 1-5 - [c4]Álvaro Mateos, Joaquín Dopazo:
An Approach to the Modular Structure of Cancer. Spanish Bioinformatics Conference 2004: 161-165 - 2003
- [j3]Michal Karzynski, Álvaro Mateos, Javier Herrero, Joaquín Dopazo:
Using a Genetic Algorithm and a Perceptron for Feature Selection and Supervised Class Learning in DNA Microarray Data. Artif. Intell. Rev. 20(1-2): 39-51 (2003) - [j2]Javier Herrero, Fátima Al-Shahrour, Ramón Díaz-Uriarte, Álvaro Mateos, Juan M. Vaquerizas, Javier Santoyo, Joaquín Dopazo:
GEPAS: a web-based resource for microarray gene expression data analysis. Nucleic Acids Res. 31(13): 3461-3467 (2003) - [j1]Lucía Conde, Álvaro Mateos, Javier Herrero, Joaquín Dopazo:
Improved Class Prediction in DNA Microarray Gene Expression Data by Unsupervised Reduction of the Dimensionality followed by Supervised Learning with a Perceptron. J. VLSI Signal Process. 35(3): 245-253 (2003) - [c3]Fátima Al-Shahrour, Javier Herrero, Álvaro Mateos, Javier Santoyo, Ramón Díaz-Uriarte, Joaquín Dopazo:
Using gene ontology on genome-scale studies to find significant associations of biologically relevant terms to groups of genes. NNSP 2003: 43-52 - 2002
- [c2]Álvaro Mateos, Javier Herrero, Joaquín Dopazo:
Using Perceptrons for Supervised Classification of DNA Microarray Samples: Obtaining the Optimal Level of Information and Finding Differentially Expressed Genes. ICANN 2002: 577-582 - [c1]Lucía Conde, Álvaro Mateos, Javier Herrero, Joaquín Dopazo:
Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data. NNSP 2002: 77-86
Coauthor Index
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