Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 6th European Conference, EvoBIO 2008, Naples, Italy, March 26-28, 2008, ProceedingsSpringer Science & Business Media, 14 thg 3, 2008 - 213 trang 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). |
Nội dung
A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification | 1 |
Using Ant Colony OptimizationBased Selected Features for Predicting Postsynaptic Activity in Proteins | 12 |
Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA | 24 |
A Full Parsing Based Approach to Protein Relation Extraction | 36 |
Improving the Performance of Hierarchical Classification with Swarm Intelligence | 48 |
Protein Interaction Inference Using Particle Swarm Optimization Algorithm | 61 |
Divide Align and FullSearch for Discovering Conserved Protein Complexes | 71 |
Detection of Quantitative Trait Associated Genes Using Cluster Analysis | 83 |
Mining Gene Expression Patterns for the Discovery of Overlapping Clusters | 117 |
Development and Evaluation of an OpenEnded Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Disea... | 129 |
Gene Selection and Cancer Microarray Data Classification Via MixedInteger Optimization | 141 |
Detection of Protein Complexes in Protein Interaction Networks Using nClubs | 153 |
Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control | 165 |
Enhancing Parameter Estimation of Biochemical Networks by Exponentially Scaled Search Steps | 177 |
A WrapperBased Feature Selection Method for ADMET Prediction Using Evolutionary Computing | 188 |
On the Convergence of Protein Structure and Dynamics Statistical Learning Studies of Pseudo Folding Pathways | 200 |
Frequent Subsplit Representation of LeafLabelled Trees | 95 |
Inference on Missing Values in Genetic Networks Using HighThroughput Data | 106 |
Author Index | 212 |
Ấn bản in khác - Xem tất cả
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics ... Elena Marchiori,Jason H. Moore Xem trước bị giới hạn - 2008 |
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics Không có bản xem trước - 2013 |
Thuật ngữ và cụm từ thông dụng
accuracy alignment analysis Ant Colony Optimization approach assigned Bioinformatics biological BPFTs cancer chromosomes classification algorithm classifier node clustering algorithms computational correlation corresponding data mining database dataset descriptors detect discovered DivAfull domain enzymes epistasis evaluation EvoBIO evolutionary example experimental feature selection folding function gene expression gene expression data genetic genome genomeSIMLA graph greedy hierarchical IAMB IAMB(X IAMBFDR interaction networks iteration k-means algorithm kernel leafset LNCS machine learning matrix MaWish method mutation n-club number of clusters obtained operators optimisation orthologous p-value parameters parsing particle swarm Particle Swarm Optimization patterns performance phenotype pheromone polynomial population PPI networks prediction problem Programming proposed protein complexes protein folding protein interaction protein-protein interaction pseudo-folding PSO/ACO random represents sample simulation SNPs solutions splits splitset statistical structure subgraph support vector machines Swarm Intelligence Table techniques tion tissues trees values variables vector weight