LEADER 05613nam 22008295 450 001 9910483278703321 005 20200629172453.0 010 $a3-319-09192-1 024 7 $a10.1007/978-3-319-09192-1 035 $a(CKB)3710000000219415 035 $a(SSID)ssj0001338810 035 $a(PQKBManifestationID)11860059 035 $a(PQKBTitleCode)TC0001338810 035 $a(PQKBWorkID)11345036 035 $a(PQKB)11400595 035 $a(DE-He213)978-3-319-09192-1 035 $a(MiAaPQ)EBC5595145 035 $a(PPN)18062587X 035 $a(EXLCZ)993710000000219415 100 $a20140813d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aPattern Recognition in Bioinformatics $e9th IAPR International Conference, PRIB 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings /$fedited by Matteo Comin, Lukas Käll, Elena Marchiori, Alioune Ngom, Jagath Chandana Rajapakse 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XII, 135 p. 29 illus.) 225 1 $aLecture Notes in Bioinformatics ;$v8626 300 $aIncludes index. 311 $a3-319-09191-3 327 $aFULL PAPERS -- Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions -- Using Topology Information for Protein-Protein Interaction Prediction -- Biases of drug{target interaction network data -- Logol: Expressive Pattern Matching in sequences Application to Ribosomal Frameshift Modeling -- Evolutionary Algorithm based on New Crossover for the Biclustering of Gene Expression Data -- SFFS-SW: A feature selection algorithm exploring the small-world properties of GNs -- CytomicsDB: A Metadata-based storage and retrieval approach for High-Throughput Screening Experiments -- CUDAGRN: Parallel Speedup of Inferring Large Gene Regulatory -- Networks from Expression Data Using Random Forest -- SHORT ABSTRACTS -- Analysis of miRNA expression profiles in breast cancer using biclustering -- Gram-positive and Gram-negative Subcellular Localization Using Rotation Forest and Physicochemical-based Features -- Data Driven Feature Selection for RNA-Seq Differential Expression Analysis -- Intramuscular fat percentage estimation through ultrasound images -- An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in Acute Myeloid Leukemia -- Improving performance of the eXtasy model by hierarchical sampling -- Popovic et al.Ensemble Neural Networks Scoring Functions for Accurate Binding Affinity -- Prediction of Protein-Ligand Complexes -- Integration of Gene Expression and DNA-methylation Profiles Improves Molecular Subtype Classification in Acute Myeloid Leukemia -- The Relative Vertex-to-Vertex Clustering Value- A New Criterion for the Fast Detection of Functional Modules in Protein Interaction Networks. 330 $aThis book constitutes the refereed proceedings of the 8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2014, held in Stockholm, Sweden in August 2014. The 9 revised full papers and 9 revised short papers presented were carefully reviewed and selected from 29 submissions. The focus of the conference was on the latest Research in Pattern Recognition and Computational Intelligence-Based Techniques Applied to Problems in Bioinformatics and Computational Biology. 410 0$aLecture Notes in Bioinformatics ;$v8626 606 $aBioinformatics 606 $aHealth informatics 606 $aPattern recognition 606 $aData mining 606 $aAlgorithms 606 $aArtificial intelligence 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/H28009 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aBioinformatics. 615 0$aHealth informatics. 615 0$aPattern recognition. 615 0$aData mining. 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 14$aComputational Biology/Bioinformatics. 615 24$aHealth Informatics. 615 24$aPattern Recognition. 615 24$aData Mining and Knowledge Discovery. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aArtificial Intelligence. 676 $a570.285 702 $aComin$b Matteo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKäll$b Lukas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMarchiori$b Elena$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNgom$b Alioune$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRajapakse$b Jagath Chandana$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483278703321 996 $aPattern Recognition in Bioinformatics$9772456 997 $aUNINA