Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics -

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Résumé :
The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences data in order to unravel the mysteries of biological function, leading to new drugs and therapies for human disease. Life sciences data come in the form of biological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c infor- tioninagivendatasetinordertogeneratenewinterestingknowledge.Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 6th EuropeanConference on EvolutionaryComputation, Machine Learning, andDataMininginBioinformatics(EvoBIO2008)wastobringtogetherexperts from these ?elds in order to discuss new and novelmethods for tackling complex biological problems. The 6th EvoBIO conference was held in Naples, Italy on March 26-28, 2008 at the ?Centro Congressi di Ateneo Federico II?. EvoBIO 2008 was held jointly with the 11th European Conference on Genetic Programming (EuroGP 2008), the 8th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2008), and the Evo Workshops. Collectively, the conf- ences and workshops were organized under the name Evo* (www.evostar.org).

Sommaire:
A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification.- Using Ant Colony Optimization-Based Selected Features for Predicting Post-synaptic Activity in Proteins.- Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA.- DEEPER: A Full Parsing Based Approach to Protein Relation Extraction.- Improving the Performance of Hierarchical Classification with Swarm Intelligence.- Protein Interaction Inference Using Particle Swarm Optimization Algorithm.- Divide, Align and Full-Search for Discovering Conserved Protein Complexes.- Detection of Quantitative Trait Associated Genes Using Cluster Analysis.- Frequent Subsplit Representation of Leaf-Labelled Trees.- Inference on Missing Values in Genetic Networks Using High-Throughput Data.- Mining Gene Expression Patterns for the Discovery of Overlapping Clusters.- Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Diseases.- Gene Selection and Cancer Microarray Data Classification Via Mixed-Integer Optimization.- Detection of Protein Complexes in Protein Interaction Networks Using n-Clubs.- Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control.- Enhancing Parameter Estimation of Biochemical Networks by Exponentially Scaled Search Steps.- A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing.- On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways....