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The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases and the analysis of life sciences data in order to unravel the mysteries of biological function. Computer science methods such as patternrecognition,machinelearning,anddatamining haveagreatdealtoo?er the ?eld of bioinformatics. The Pattern Recognition in Bioinformatics (PRIB) meeting was established in 2006 under the auspices of the International Asso- ation of Pattern Recognition (IAPR) to create a focus for the application and development of computer science methods to life science data. The 5th PRIB conference was held in Nijmegen, The Netherlands, on 22 24 September 2010. A total of 46 papers were submitted to the conference for peer review. Of those, 38 (83%) were accepted for publication in these proceedings. The invited speakers were Rita Casadio (Bologna Biocomputing Group, Italy), Florence d Alch e-Buc (Universit e d Evry-Val d Essonne, France), Daniel - son (Tu bingen University, Germany), and Natasa Przulj (Imperial College L- don, UK). Tutorials weredeliveredby Concettina Guerra (Universit` adi Padova, Italy), Clarisse Dhaenens (Laboratoire LIFL/INRIA, France), Laetitia Jourdan (Laboratoire LIFL/INRIA, France), Neil Lawrence (University of Manchester, UK), and Dick de Ridder (Delft University of Technology).
List of contents
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 ExpertKnowledge 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.
