01413nas a2200181   4500008004100000245005600041210005500097300001100152490000700163520072100170653007700891653008700968100001701055700001501072700002101087700001701108856010601125       2005                        eng d00aHCAD, closing the gap between breakpoints and genes0 aHCAD closing the gap between breakpoints and genes  aD511-30 v333 aRecurrent 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/.10a*Chromosome Breakage Chromosome Disorders/diagnosis/*genetics *Databases10aGenetic Genes *Genetic Predisposition to Disease Humans PubMed Systems Integration1 aHoffmann, R.1 aDopazo, J.1 aCigudosa, J., C.1 aValencia, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1560825002638nas a2200157   4500008004100000245009500041210006900136300001100205490000700216520196500223653013802188100001602326700001702342700001502359856010602374       2001                        eng d00aA hierarchical unsupervised growing neural network for clustering gene expression patterns0 ahierarchical unsupervised growing neural network for clustering   a126-360 v173 aMOTIVATION: 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.10a*Algorithms Automatic Data Processing *Gene Expression Profiling *Neural Networks (Computer) *Oligonucleotide Array Sequence Analysis1 aHerrero, J.1 aValencia, A.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11238068