Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. 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 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7 9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo ( evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology.

Fast track conference proceeding

Variable Genetic Operator Search for the Molecular Docking Problem.- Variable Genetic Operator Search for the Molecular Docking Problem.- Role of Centrality in Network-Based Prioritization of Disease Genes.- Parallel Multi-Objective Approaches for Inferring Phylogenies.- An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction.- Finding Gapped Motifs by a Novel Evolutionary Algorithm.- Top-Down Induction of Phylogenetic Trees.- A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationships.- Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci.- Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions.- Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques.- Correlation-Based Scatter Search for Discovering Biclusters from Gene Expression Data.- A Local Search Appproach for Transmembrane Segment and Signal Peptide Discrimination.- A Replica Exchange Monte Carlo Algorithm for the Optimization of Secondary Structure Packing in Proteins.- Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments.- Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models.- Posters.- The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics.- Artificial Immune Systems for Epistasis Analysis in Human Genetics.- Metaheuristics for Strain Optimization Using Transcriptional Information Enriched Metabolic Models.- Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study.- Towards Automatic Detecting ofOverlapping Genes - Clustered BLAST Analysis of Viral Genomes.- Investigating Populational Evolutionary Algorithms to Add Vertical Meaning in Phylogenetic Trees.