The proceedings contain 12 papers. The topics discussed include: MicroRNA or not MicroRNA?;hierarchical multilabel protein function prediction using local neural networks;identifying significant features in HIV sequen...
ISBN:
(纸本)9783642228247
The proceedings contain 12 papers. The topics discussed include: MicroRNA or not MicroRNA?;hierarchical multilabel protein function prediction using local neural networks;identifying significant features in HIV sequence to predict patients' response to therapies;gene prediction by multiple spliced alignment;a new algorithm for sparse suffix trees;analysis and implementation of sorting by transpositions using permutation trees;improved gene expression clustering with the parameter-free PKNNG metric;efficiently querying protein sequences with the proteinus index;SciPhy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes;a conceptual model for transcriptome high-throughput sequencing pipeline;a conceptual many tasks computing architecture to execute molecular docking simulations of a fully-flexible receptor model;and kGC: finding groups of homologous genes across multiple genomes.
Signal transduction pathways control cellular responses to stimuli, but it is unclear how molecular information is processed as a network. Large-scale collection and systematization of such data is likely to have a gr...
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The deterioration of metal due to microbial activity is termed biocorrosion or microbially influenced corrosion (MIC). Owing to its economic and environmental importance, MIC has been the subject of extensive studies ...
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Gene order changes under rearrangement events such as inversions and transpositions have attracted increasing attention as a new type of data for phylogenetic analysis. Since these events are rare, they allow the reco...
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ISBN:
(纸本)9781424498963
Gene order changes under rearrangement events such as inversions and transpositions have attracted increasing attention as a new type of data for phylogenetic analysis. Since these events are rare, they allow the reconstruction of evolutionary history far back in time. Many software have been developed for the inference of gene order phylogenies, including widely used maximum parsimony methods such as GRAPPA and MGR. However, these methods confronted great difficulties in dealing with emerging large nuclear genomes. In this study, we proposed three simple yet powerful maximum likelihood(ML) based methods for phylogenetic reconstruction by first encoding the gene orders into binary or multistate strings based on gene adjacency information presented in the given genomes and further converting these strings into molecular sequences. RAxML is at last used to compute the maximum likelihood phylogeny. We conducted extensive experiments using simulated datasets and found that although the multistate encoding is more complex and more time-consuming, it did not improve accuracy over the methods using simpler binary encodings. Among all methods tested in our experiments, MLBE is of the most accuracy in most cases and often returns phylogenies without errors. ML methods is also fast and in the most difficult case only takes up to three days to compute datasets with 40 genomes, making it very suitable for large scale analysis. We give three simple and robust phylogenetic reconstruction methods using different encodings based on maximum likelihood which has not been successfully applied for gene orderings before. Our development of these ML methods showed great potential in gene order analysis with respect to the high accuracy and stability, although formal mathematical and statistical analysis of these methods are much desired.
This paper presents a significant multifractal feature description based texture discriminating technique to examine the cancer and non-cancer regions in pathological images. We acquired the characteristics (local sin...
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K nearest neighbor algorithm (k-NN) is an instance-based lazy classifier that does not need to delineate the entire boundaries between classes. Thus some classification tasks that constantly need a training procedure ...
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In bioinformatics fields, Predicting protein subcellular location is an important task, because protein has to be located in its proper position in a cell to perform its biological functions. Therefore, predicting pro...
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