@article {17935148,
	title = {Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans},
	journal = {Hum Mutat},
	volume = {29},
	number = {1},
	year = {2008},
	note = {Capriotti, Emidio Arbiza, Leonardo Casadio, Rita Dopazo, Joaquin Dopazo, Hernan Marti-Renom, Marc A Evaluation Studies Research Support, Non-U.S. Gov{\textquoteright}t United States Human mutation Hum Mutat. 2008 Jan;29(1):198-204.},
	pages = {198-204},
	abstract = {Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82\% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.},
	keywords = {Algorithms Codon/genetics Computational Biology/*methods *DNA Mutational Analysis Databases, Human Humans Iduronic Acid/analogs \& derivatives/metabolism *Point Mutation Polymorphism, Molecular *Genetic Predisposition to Disease Genetic Variation Genome, Protein *Evolution, Single Nucleotide Proteins/chemistry/*genetics Tumor Suppressor Protein p53/genetics},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17935148},
	author = {E. Capriotti and Arbiza, L. and Casadio, R. and Dopazo, J. and H. Dopazo and M. A. Marti-Renom}
}
@inbook {491,
	title = {Use of GO Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data},
	booktitle = {Microarray data analysis III},
	year = {2003},
	pages = {233-247},
	publisher = {Kluwer Academic, K. F. Johnson and S. M. Lin},
	organization = {Kluwer Academic, K. F. Johnson and S. M. Lin},
	author = {D{\'\i}az-Uriarte, R and Fatima Al-Shahrour and Dopazo, J.}
}
@inbook {492,
	title = {Using Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes},
	booktitle = {Neural Networks for Signal Processing XIII},
	year = {2003},
	pages = {43-52},
	publisher = {IEEE Press},
	organization = {IEEE Press},
	address = {New York, USA},
	keywords = {babelomics},
	author = {Fatima Al-Shahrour and Herrero, J. and A. Mateos and J. Santoyo and D{\'\i}az-Uriarte, R and Dopazo, J.}
}
@conference {556,
	title = {Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data},
	booktitle = {Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing},
	year = {2002},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Martigny, Switzerland},
	doi = {10.1109/NNSP.2002.1030019},
	url = {http://ieeexplore.ieee.org/document/1030019/http://xplorestaging.ieee.org/ielx5/8007/22134/01030019.pdf?arnumber=1030019},
	author = {Conde, L. and Mateos, A. and Herrero, J. and Dopazo, J.}
}
@inbook {496,
	title = {Using perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes},
	booktitle = {ICANN 2002, LNCS 2415},
	year = {2002},
	pages = {577-582},
	publisher = {J.R. Dorronsoro},
	organization = {J.R. Dorronsoro},
	author = {A. Mateos and Herrero, J. and Dopazo, J.}
}
