@article {18238804,
	title = {Interoperability with Moby 1.0{\textendash}it{\textquoteright}s better than sharing your toothbrush!},
	journal = {Brief Bioinform},
	volume = {9},
	number = {3},
	year = {2008},
	note = {<p>BioMoby Consortium Wilkinson, Mark D Senger, Martin Kawas, Edward Bruskiewich, Richard Gouzy, Jerome Noirot, Celine Bardou, Philippe Ng, Ambrose Haase, Dirk Saiz, Enrique de Andres Wang, Dennis Gibbons, Frank Gordon, Paul M K Sensen, Christoph W Carrasco, Jose Manuel Rodriguez Fernandez, Jose M Shen, Lixin Links, Matthew Ng, Michael Opushneva, Nina Neerincx, Pieter B T Leunissen, Jack A M Ernst, Rebecca Twigger, Simon Usadel, Bjorn Good, Benjamin Wong, Yan Stein, Lincoln Crosby, William Karlsson, Johan Royo, Romina Parraga, Ivan Ramirez, Sergio Gelpi, Josep Lluis Trelles, Oswaldo Pisano, David G Jimenez, Natalia Kerhornou, Arnaud Rosset, Roman Zamacola, Leire Tarraga, Joaquin Huerta-Cepas, Jaime Carazo, Jose Maria Dopazo, Joaquin Guigo, Roderic Navarro, Arcadi Orozco, Modesto Valencia, Alfonso Claros, M Gonzalo Perez, Antonio J Aldana, Jose Rojano, M Mar Fernandez-Santa Cruz, Raul Navas, Ismael Schiltz, Gary Farmer, Andrew Gessler, Damian Schoof, Heiko Groscurth, Andreas Research Support, Non-U.S. Gov{\textquoteright}t Review England Briefings in bioinformatics Brief Bioinform. 2008 May;9(3):220-31. Epub 2008 Jan 31.</p>},
	pages = {220-31},
	abstract = {<p>The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.</p>},
	keywords = {Computational Biology/*methods *Database Management Systems *Databases, Factual Information Storage and Retrieval/*methods *Internet *Programming Languages Systems Integration},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18238804},
	author = {Wilkinson, M. D. and Senger, M. and Kawas, E. and Bruskiewich, R. and Gouzy, J. and Noirot, C. and Bardou, P. and Ng, A. and Haase, D. and Saiz Ede, A. and Wang, D. and Gibbons, F. and Gordon, P. M. and Sensen, C. W. and Carrasco, J. M. and Fernandez, J. M. and Shen, L. and Links, M. and Ng, M. and Opushneva, N. and Neerincx, P. B. and Leunissen, J. A. and Ernst, R. and Twigger, S. and Usadel, B. and Good, B. and Wong, Y. and Stein, L. and Crosby, W. and Karlsson, J. and Royo, R. and Parraga, I. and Ramirez, S. and Gelpi, J. L. and Trelles, O. and Pisano, D. G. and Jimenez, N. and Kerhornou, A. and Rosset, R. and Zamacola, L. and Tarraga, J. and Huerta-Cepas, J. and Carazo, J. M. and Dopazo, J. and R. Guigo and Navarro, A. and Orozco, M. and Valencia, A. and Claros, M. G. and Perez, A. J. and Aldana, J. and Rojano, M. M. and Fernandez-Santa Cruz, R. and Navas, I. and Schiltz, G. and Farmer, A. and Gessler, D. and Schoof, H. and Groscurth, A.}
}
@article {15608250,
	title = {HCAD, closing the gap between breakpoints and genes},
	journal = {Nucleic Acids Res},
	volume = {33},
	number = {Database issue},
	year = {2005},
	note = {Hoffmann, Robert Dopazo, Joaquin Cigudosa, Juan C Valencia, Alfonso Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2005 Jan 1;33(Database issue):D511-3.},
	pages = {D511-3},
	abstract = {Recurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/.},
	keywords = {*Chromosome Breakage Chromosome Disorders/diagnosis/*genetics *Databases, Genetic Genes *Genetic Predisposition to Disease Humans PubMed Systems Integration},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15608250},
	author = {Hoffmann, R. and Dopazo, J. and Cigudosa, J. C. and Valencia, A.}
}
@article {12824315,
	title = {EVA: Evaluation of protein structure prediction servers},
	journal = {Nucleic Acids Res},
	volume = {31},
	number = {13},
	year = {2003},
	note = {Koh, Ingrid Y Y Eyrich, Volker A Marti-Renom, Marc A Przybylski, Dariusz Madhusudhan, Mallur S Eswar, Narayanan Grana, Osvaldo Pazos, Florencio Valencia, Alfonso Sali, Andrej Rost, Burkhard 1-P50-GM62413-01/GM/NIGMS NIH HHS/United States 5-P20-LM7276/LM/NLM NIH HHS/United States P50 GM62529/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States R01-GM63029-01/GM/NIGMS NIH HHS/United States Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, Non-P.H.S. Research Support, U.S. Gov{\textquoteright}t, P.H.S. England Nucleic acids research Nucleic Acids Res. 2003 Jul 1;31(13):3311-5.},
	pages = {3311-5},
	abstract = {EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.},
	keywords = {Automation Databases, Protein, Protein Internet *Protein Conformation Protein Folding Protein Structure, Protein Structural Homology, Secondary Proteins/chemistry Reproducibility of Results *Sequence Analysis},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12824315},
	author = {Koh, I. Y. and Eyrich, V. A. and M. A. Marti-Renom and Przybylski, D. and Madhusudhan, M. S. and Eswar, N. and Grana, O. and Pazos, F. and Valencia, A. and Sali, A. and Rost, B.}
}
@article {12141992,
	title = {Bioinformatics methods for the analysis of expression arrays: data clustering and information extraction},
	journal = {J Biotechnol},
	volume = {98},
	number = {2-3},
	year = {2002},
	note = {Tamames, Javier Clark, Dominic Herrero, Javier Dopazo, Joaquin Blaschke, Christian Fernandez, Jose M Oliveros, Juan C Valencia, Alfonso Review Netherlands Journal of biotechnology J Biotechnol. 2002 Sep 25;98(2-3):269-83.},
	pages = {269-83},
	abstract = {Expression arrays facilitate the monitoring of changes in the expression patterns of large collections of genes. The analysis of expression array data has become a computationally-intensive task that requires the development of bioinformatics technology for a number of key stages in the process, such as image analysis, database storage, gene clustering and information extraction. Here, we review the current trends in each of these areas, with particular emphasis on the development of the related technology being carried out within our groups.},
	keywords = {Abstracting and Indexing as Topic/methods *Cluster Analysis *Database Management Systems Databases, Computer-Assisted/methods Information Storage and Retrieval/*methods Internet Medline National Library of Medicine (U.S.) Oligonucleotide Array Sequence Analysis/*methods United States, Genetic Gene Expression Gene Expression Profiling/*methods Image Processing},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=12141992},
	author = {J. Tamames and Clark, D. and Herrero, J. and Dopazo, J. and Blaschke, C. and Fernandez, J. M. and Oliveros, J. C. and Valencia, A.}
}
@article {11751240,
	title = {EVA: continuous automatic evaluation of protein structure prediction servers},
	journal = {Bioinformatics},
	volume = {17},
	number = {12},
	year = {2001},
	note = {Eyrich, V A Marti-Renom, M A Przybylski, D Madhusudhan, M S Fiser, A Pazos, F Valencia, A Sali, A Rost, B England Bioinformatics (Oxford, England) Bioinformatics. 2001 Dec;17(12):1242-3.},
	pages = {1242-3},
	abstract = {Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY: http://cubic.bioc.columbia.edu/eva. CONTACT: eva@cubic.bioc.columbia.edu},
	keywords = {Automation Internet *Protein Conformation Proteins/*analysis *Software},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11751240},
	author = {Eyrich, V. A. and M. A. Marti-Renom and Przybylski, D. and Madhusudhan, M. S. and Fiser, A. and Pazos, F. and Valencia, A. and Sali, A. and Rost, B.}
}
@article {11238068,
	title = {A hierarchical unsupervised growing neural network for clustering gene expression patterns},
	journal = {Bioinformatics},
	volume = {17},
	number = {2},
	year = {2001},
	note = {Herrero, J Valencia, A Dopazo, J Research Support, Non-U.S. Gov{\textquoteright}t England Bioinformatics (Oxford, England) Bioinformatics. 2001 Feb;17(2):126-36.},
	pages = {126-36},
	abstract = {MOTIVATION: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226-233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. RESULTS: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. AVAILABILITY: A server running the program can be found at: http://bioinfo.cnio.es/sotarray.},
	keywords = {*Algorithms Automatic Data Processing *Gene Expression Profiling *Neural Networks (Computer) *Oligonucleotide Array Sequence Analysis},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11238068},
	author = {Herrero, J. and Valencia, A. and Dopazo, J.}
}
