Flexible protein folding by ant colony optimization

Protein structure prediction is one of the most challenging topics in bioinformatics. As the protein structure is found to be closely related to its functions, predicting the folding structure of a protein to judge its functions is meaningful to the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice model. Different from the previous ant algorithms for PFPs, the pheromones in the proposed algorithm are placed on the arcs connecting adjacent squares in the lattice. Such pheromone placement model is similar to the one used in the traveling salesmen problems (TSPs), where pheromones are released on the arcs connecting the cities. Moreover, the collaboration of effective heuristic and pheromone strategies greatly enhances the performance of the algorithm so that the algorithm can achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems

Ant Colony Optimisation for Exploring Logical Gene-Gene Associations in Genome Wide Association Studies.

In this paper a search for the logical variants of gene-gene interactions in genome-wide association study (GWAS) data using ant colony optimisation is proposed. The method based on stochastic algorithms is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover logical operations between combinations of single nucleotide polymorphisms that can discriminate Type II diabetes. A variety of logical combinations are explored and the best discovered associations are found within reasonable computational time and are shown to be statistically significantThis study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. The work contained in this paper was funded by an EPSRC First Grant (EP/J007439/1) and we acknowledge their kind support

Subset-Based Ant Colony Optimisation for the Discovery of Gene-Gene Interactions in Genome Wide Association Studies

In this paper an ant colony optimisation approach for the discovery of gene-gene interactions in genome-wide association study (GWAS) data is proposed. The subset-based approach includes a novel encoding mechanism and tournament selection to analyse full scale GWAS data consisting of hundreds of thousands of variables to discover associations between combinations of small DNA changes and Type II diabetes. The method is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover combinations that are statistically significant and biologically relevant within reasonable computational time.The work contained in this paper was supported by an EPSRC First Grant (EP/J007439/1). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the inves- tigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113

AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.</p> <p>Findings</p> <p>AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in a case-control design. Based on some practical epistatic models, AntEpiSeeker has performed very well.</p> <p>Conclusions</p> <p>AntEpiSeeker is a powerful and efficient tool for large-scale association studies and can be downloaded from <url>http://nce.ads.uga.edu/~romdhane/AntEpiSeeker/index.html</url>.</p

Protein folding in hydrophobic-polar lattice model: a flexible ant colony optimization approach

This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms

Development of Multiple Polymorphic Microsatellite Markers for Ceratina calcarata (Hymenoptera: Apidae) Using Genome-Wide Analysis

The small carpenter bee, Ceratina calcarata (Robertson), is a widespread native pollinator across eastern North America. The behavioral ecology and nesting biology of C. calcarata has been relatively well-studied and the species is emerging as a model organism for both native pollinator and social evolution research. C. calcarata is subsocial: reproductively mature females provide extended maternal care to their brood. As such, studies of C. calcarata may also reveal patterns of relatedness and demography unique to primitively social Hymenoptera. Here, we present 21 microsatellite loci, isolated from the recently completed C. calcarata genome. Screening in 39 individuals across their distribution revealed that no loci were in linkage disequilibrium, nor did any deviate significantly from Hardy-Weinberg following sequential Bonferroni correction. Allele count ranged from 2 to 14, and observed and expected heterozygosities ranged from 0.08 to 0.82 (mean 0.47) and 0.26 to 0.88 (mean 0.56), respectively. These markers will enable studies of population-wide genetic structuring across C. calcarata’s distribution. Such tools will also allow for exploration of between and within-colony relatedness in this subsocial native pollinator

Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing

This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been implemented as part of a BOINC volunteer computing project, allowing large scale evolution. During a period of two months, over 4,500 volunteered computers on the Citizen Science Grid trained over 120,000 CNNs and evolved networks reaching 98.32% test data accuracy on the MNIST handwritten digits dataset. These results are even stronger as the backpropagation strategy used to train the CNNs was fairly rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these were initial test runs done without refinement of the backpropagation hyperparameters. Further, the EXACT evolutionary strategy is independent of the method used to train the CNNs, so they could be further improved by advanced techniques like elastic distortions, pretraining and dropout. The evolved networks are also quite interesting, showing "organic" structures and significant differences from standard human designed architectures.Comment: 17 pages, 13 figures. Submitted to the 2017 Genetic and Evolutionary Computation Conference (GECCO 2017