Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 8th European Confere Clara Pizzuti,Marylyn D. Ritchie,Mario G

metamorphose

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書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics影響因子(影響力)學(xué)科排名




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書目名稱Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics讀者反饋學(xué)科排名

Comprise

Role of Centrality in Network-Based Prioritization of Disease Genesthat the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Random walk and network propagation

新義

Parallel Multi-Objective Approaches for Inferring Phylogeniess. Several single optimality criterion have been proposed for the phylogenetic reconstruction problem. However, different criteria may lead to conflicting phylogenies. In this scenario, a multi-objective approach can be useful since it could produce a set of optimal trees according to multiple crite

confederacy

An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Predictionputational biology. A new evolutionary model based on hill-climbing genetic operators is proposed to address the hydrophobic - polar model of the protein folding problem. The introduced model ensures an efficient exploration of the search space by implementing a problem-specific crossover operator a

西瓜

Finding Gapped Motifs by a Novel Evolutionary Algorithming the complex gene regulatory networks and understanding gene functions. In this work, we develop a novel motif finding algorithm based on a population-based stochastic optimization technique called Particle Swarm Optimization (PSO), which has been shown to be effective in optimizing difficult mul

有機(jī)體

有機(jī)體

A Model Free Method to Generate Human Genetics Datasets with Complex Gene-Disease Relationshipst to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variations and the task of modeling interactions between them. We and others have previously developed algorithms to

Atmosphere

Ligament

Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such interactive models present an important analytical challenge, requiring that methods perform both variable selection and statistical modeling to generate testable

Immunotherapy

Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Cogredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many “gene expression signatures” have been developed, i.e. sets of genes whose expression values in