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05520nam a22005775i 4500 |
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978-3-642-16001-1 |
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DE-He213 |
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20151204171931.0 |
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cr nn 008mamaa |
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100914s2010 gw | s |||| 0|eng d |
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|a 9783642160011
|9 978-3-642-16001-1
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|a 10.1007/978-3-642-16001-1
|2 doi
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|a QH301-705
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|a SCI086000
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|a 570
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|a Pattern Recognition in Bioinformatics
|h [electronic resource] :
|b 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings /
|c edited by Tjeerd M. H. Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes.
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| 264 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2010.
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| 300 |
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|a XII, 442 p. 153 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 6282
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| 505 |
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|a Classification of Biological Sequences -- Sequence-Based Prediction of Protein Secretion Success in Aspergillus niger -- Machine Learning Study of DNA Binding by Transcription Factors from the LacI Family -- Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search -- Structured Output Prediction of Anti-cancer Drug Activity -- SLiMSearch: A Webserver for Finding Novel Occurrences of Short Linear Motifs in Proteins, Incorporating Sequence Context -- Towards 3D Modeling of Interacting TM Helix Pairs Based on Classification of Helix Pair Sequence -- Optimization Algorithms for Identification and Genotyping of Copy Number Polymorphisms in Human Populations -- Preservation of Statistically Significant Patterns in Multiresolution 0-1 Data -- Novel Machine Learning Methods for MHC Class I Binding Prediction -- Unsupervised Learning Methods for Biological Sequences -- SIMCOMP: A Hybrid Soft Clustering of Metagenome Reads -- The Complexity and Application of Syntactic Pattern Recognition Using Finite Inductive Strings -- An Algorithm to Find All Identical Motifs in Multiple Biological Sequences -- Discovery of Non-induced Patterns from Sequences -- Exploring Homology Using the Concept of Three-State Entropy Vector -- A Maximum-Likelihood Formulation and EM Algorithm for the Protein Multiple Alignment Problem -- Polynomial Supertree Methods Revisited -- Enhancing Graph Database Indexing by Suffix Tree Structure -- Learning Methods for Gene Expression and Mass Spectrometry Data -- Semi-Supervised Graph Embedding Scheme with Active Learning (SSGEAL): Classifying High Dimensional Biomedical Data -- Iterated Local Search for Biclustering of Microarray Data -- Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of Expression Microarray -- Measuring the Quality of Shifting and Scaling Patterns in Biclusters -- Frequent Episode Mining to Support Pattern Analysis in Developmental Biology -- Time Series Gene Expression Data Classification via L 1-norm Temporal SVM -- Bioimaging -- Sub-grid and Spot Detection in DNA Microarray Images Using Optimal Multi-level Thresholding -- Quantification of Cytoskeletal Protein Localization from High-Content Images -- Pattern Recognition for High Throughput Zebrafish Imaging Using Genetic Algorithm Optimization -- Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis -- Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging -- Molecular Structure Prediction -- A Matrix Algorithm for RNA Secondary Structure Prediction -- Exploiting Long-Range Dependencies in Protein ?-Sheet Secondary Structure Prediction -- Alpha Helix Prediction Based on Evolutionary Computation -- An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps -- Protein Protein Interaction and Network Inference -- Biological Protein-Protein Interaction Prediction Using Binding Free Energies and Linear Dimensionality Reduction -- Employing Publically Available Biological Expert Knowledge from Protein-Protein Interaction Information -- SFFS-MR: A Floating Search Strategy for GRNs Inference -- Revisiting the Voronoi Description of Protein-Protein Interfaces: Algorithms -- MC4: A Tempering Algorithm for Large-Sample Network Inference -- Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks.
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| 650 |
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|a Life sciences.
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| 650 |
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|a Medicine.
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| 650 |
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|a Health informatics.
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| 650 |
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0 |
|a Data mining.
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| 650 |
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|a Pattern recognition.
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| 650 |
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|a Bioinformatics.
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| 650 |
1 |
4 |
|a Life Sciences.
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| 650 |
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|a Life Sciences, general.
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| 650 |
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4 |
|a Medicine/Public Health, general.
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| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
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| 650 |
2 |
4 |
|a Computational Biology/Bioinformatics.
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| 650 |
2 |
4 |
|a Health Informatics.
|
| 650 |
2 |
4 |
|a Pattern Recognition.
|
| 700 |
1 |
|
|a Dijkstra, Tjeerd M. H.
|e editor.
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| 700 |
1 |
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|a Tsivtsivadze, Evgeni.
|e editor.
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| 700 |
1 |
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|a Marchiori, Elena.
|e editor.
|
| 700 |
1 |
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|a Heskes, Tom.
|e editor.
|
| 710 |
2 |
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|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783642160004
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| 830 |
|
0 |
|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 6282
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-642-16001-1
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-LNC
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| 950 |
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|a Computer Science (Springer-11645)
|