LEADER 06935nam 2200601 450 001 996465879003316 005 20210318175231.0 010 $a3-540-72031-6 024 7 $a10.1007/978-3-540-72031-7 035 $a(CKB)1000000000490363 035 $a(EBL)3061663 035 $a(SSID)ssj0000316481 035 $a(PQKBManifestationID)11261405 035 $a(PQKBTitleCode)TC0000316481 035 $a(PQKBWorkID)10274984 035 $a(PQKB)10158574 035 $a(DE-He213)978-3-540-72031-7 035 $a(MiAaPQ)EBC3061663 035 $a(MiAaPQ)EBC6413340 035 $a(PPN)123161827 035 $a(EXLCZ)991000000000490363 100 $a20210318d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBioinformatics research and applications $ethird international symposium, ISBRA 2007, Atlanta, GA, USA, May 7-10, 2007 : proceedings /$fIon Ma?ndoiu, Alexander Zelikovsky (Eds.) 205 $a1st ed. 2007. 210 1$aBerlin, Germany ;$aNew York, New York State :$cSpringer,$d[2007] 210 4$d©2007 215 $a1 online resource (665 p.) 225 1 $aLecture notes in bioinformatics,$x0302-9743 ;$v4463 300 $aDescription based upon print version of record. 311 $a3-540-72030-8 320 $aIncludes bibliographical references and index. 327 $aGFBA: A Biclustering Algorithm for Discovering Value-Coherent Biclusters -- Significance Analysis of Time-Course Gene Expression Profiles -- Data-Driven Smoothness Enhanced Variance Ratio Test to Unearth Responsive Genes in 0-Time Normalized Time-Course Microarray Data -- Efficiently Finding the Most Parsimonious Phylogenetic Tree Via Linear Programming -- A Multi-Stack Based Phylogenetic Tree Building Method -- A New Linear-Time Heuristic Algorithm for Computing the Parsimony Score of Phylogenetic Networks: Theoretical Bounds and Empirical Performance -- A Bootstrap Correspondence Analysis for Factorial Microarray Experiments with Replications -- Clustering Algorithms Optimizer: A Framework for Large Datasets -- Ranking Function Based on Higher Order Statistics (RF-HOS) for Two-Sample Microarray Experiments -- Searching for Recombinant Donors in a Phylogenetic Network of Serial Samples -- Algorithm for Haplotype Inferring Via Galled-Tree Networks with Simple Galls -- Estimating Bacterial Diversity from Environmental DNA: A Maximum Likelihood Approach -- Invited Talk: Modern Homology Search -- Statistical Absolute Evaluation of Gene Ontology Terms with Gene Expression Data -- Discovering Relations Among GO-Annotated Clusters by Graph Kernel Methods -- An Empirical Comparison of Dimensionality Reduction Methods for Classifying Gene and Protein Expression Datasets -- NEURONgrid: A Toolkit for Generating Parameter-Space Maps Using NEURON in a Grid Environment -- An Adaptive Resolution Tree Visualization of Large Influenza Virus Sequence Datasets -- Wavelet Image Interpolation (WII): A Wavelet-Based Approach to Enhancement of Digital Mammography Images -- High Level Programming Environment System for Protein Structure Data -- Finding Minimal Sets of Informative Genes in Microarray Data -- Noise-Based Feature Perturbation as a Selection Method for Microarray Data -- Efficient Generation of Biologically Relevant Enriched Gene Sets -- Space and Time Efficient Algorithms to Discover Endogenous RNAi Patterns in Complete Genome Data -- A Fast Approximate Covariance-Model-Based Database Search Method for Non-coding RNA -- Extensions of Naive Bayes and Their Applications to Bioinformatics -- The Solution Space of Sorting by Reversals -- A Fast and Exact Algorithm for the Perfect Reversal Median Problem -- Genomic Signatures from DNA Word Graphs -- Enhancing Motif Refinement by Incorporating Comparative Genomics Data -- Mining Discriminative Distance Context of Transcription Factor Binding Sites on ChIP Enriched Regions -- Enhanced Prediction of Cleavage in Bovine Precursor Sequences -- Invited Talk: A Computational Study of Bidirectional Promoters in the Human Genome -- The Identification of Antisense Gene Pairs Through Available Software -- Inferring Weak Adaptations and Selection Biases in Proteins from Composition and Substitution Matrices -- Markov Model Variants for Appraisal of Coding Potential in Plant DNA -- Predicting Palmitoylation Sites Using a Regularised Bio-basis Function Neural Network -- A Novel Kernel-Based Approach for Predicting Binding Peptides for HLA Class II Molecules -- A Database for Prediction of Unique Peptide Motifs as Linear Epitopes -- A Novel Greedy Algorithm for the Minimum Common String Partition Problem -- An Efficient Algorithm for Finding Gene-Specific Probes for DNA Microarrays -- Multiple Sequence Local Alignment Using Monte Carlo EM Algorithm -- Cancer Class Discovery Using Non-negative Matrix Factorization Based on Alternating Non-negativity-Constrained Least Squares -- A Support Vector Machine Ensemble for Cancer Classification Using Gene Expression Data -- Combining SVM Classifiers Using Genetic Fuzzy Systems Based on AUC for Gene Expression Data Analysis -- A BP-SCFG Based Approach for RNA Secondary Structure Prediction with Consecutive Bases Dependency and Their Relative Positions Information -- Delta: A Toolset for the Structural Analysis of Biological Sequences on a 3D Triangular Lattice -- Statistical Estimate for the Size of the Protein Structural Vocabulary -- Coclustering Based Parcellation of Human Brain Cortex Using Diffusion Tensor MRI -- An Algorithm for Hierarchical Classification of Genes of Prokaryotic Genomes -- Using Multi Level Nearest Neighbor Classifiers for G-Protein Coupled Receptor Sub-families Prediction -- Invited Talk: Ab Initio Gene Finding Engines: What Is Under the Hood -- Reconstruction of 3D Structures from Protein Contact Maps -- A Feature Selection Algorithm Based on Graph Theory and Random Forests for Protein Secondary Structure Prediction -- DNA Sites Buried in Nucleosome Become Accessible at Room Temperature: A Discrete-Event-Simulation Based Modeling Approach -- Comparative Analysis of Gene-Coexpression Networks Across Species -- Comparative Pathway Prediction Via Unified Graph Modeling of Genomic Structure Information -- Extending the Calculus of Looping Sequences to Model Protein Interaction at the Domain Level. 410 0$aLecture notes in computer science.$pLecture notes in bioinformatics ;$v4463. 606 $aComputational biology$vCongresses 606 $aBioinformatics$vCongresses 615 0$aComputational biology 615 0$aBioinformatics 676 $a570.285 702 $aMa?ndoiu$b Ion 702 $aZelikovsky$b Alexander 712 12$aISBRA 2007 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465879003316 996 $aBioinformatics Research and Applications$9772362 997 $aUNISA LEADER 03539oam 2200589 450 001 9910132647003321 005 20221206094423.0 010 $a9782821812277$b(ebook) 010 $z9782821812260$b(paperback) 024 7 $a10.4000/books.oep.198 035 $a(CKB)3680000000164684 035 $a(SSID)ssj0000986024 035 $a(PQKBManifestationID)11544611 035 $a(PQKBTitleCode)TC0000986024 035 $a(PQKBWorkID)10933431 035 $a(PQKB)11560178 035 $a(WaSeSS)Ind00074734 035 $z(PPN)182833321 035 $a(FrMaCLE)OB-oep-198 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/55287 035 $a(PPN)180217992 035 $a(EXLCZ)993680000000164684 100 $a20160829d2012 uy | 101 0 $afre 135 $aur||#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOpinion mining et sentiment analysis méthodes et outils $eméthodes et outils /$fDominique Boullier et Audrey Lohard 210 $cOpenEdition Press$d2012 210 31$aFrance :$cOpenEdition Press,$d2012 215 $a1 online resource $cdigital, PDF file(s) 225 0 $aSciences Po, me?dialab 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$aPrint version: 9782821812260 320 $aIncludes bibliographical references. 330 $aOpinion mining is on the way to becoming a real industry, just as strategic as that of polls. The promises made are impressive: the computing power of computer tools would make it possible to follow all the evolutions of opinion on the web in real time, whatever the volume. Moreover, the linguistic processing capacities would make it possible to detect the tones of all the verbatims collected, thanks to the so-called ?sentiment analysis? methods. The state of the art of commercial and technological offers presented in this book takes account of this effervescence but also underlines its excessiveness, by taking care to distinguish the real results from the sometimes misleading promotional slogans. he book written by researchers from the medialab of Sciences Po, a laboratory specializing in the processing of the masses of data available on the web for the social sciences, also makes it possible to situate the interest of these new technical means for research, in the context of of what are now called ?digital humanities?. Finally, anxious to allow each reader to take in hand these tools, certainly powerful but with very real limits of validity, the authors describe step by step all the phases of a project mobilizing the methods of opinion mining, specifying the pitfalls. and the imperatives of intervention of human expertise, always necessary. Largely illustrated, this book should encourage researchers, public opinion and marketing professionals as well as computer scientists and specialists in ?web studies? to exchange views in order to advance these common tools. 606 $aEngineering & Applied Sciences$2HILCC 606 $aComputer Science$2HILCC 610 $aédition électronique 610 $aweb 610 $adigital humanities 610 $ausages 615 7$aEngineering & Applied Sciences 615 7$aComputer Science 700 $aBoullier$b Dominique$0801430 702 $aLohard$b Audrey 801 0$bPQKB 801 2$bUkMaJRU 912 $a9910132647003321 996 $aOpinion mining et sentiment analysis méthodes et outils$92115473 997 $aUNINA