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Evolving Graph Compression using Similarity Measures for bioinformatics Applications 13

Evolving Graph Compression using Similarity Measures for Bio...

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13th ieee Annual Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Brown, Joseph Alexander Houghten, Sheridan Collins, Tyler Kennedy Qu, Qiang Innopolis Univ Inst Informat Syst Innopolis Russia Brock Univ Dept Comp Sci St Catharines ON Canada

ISBN: (纸本)9781467394727

Many real-world graphs, including those storing various forms of biological data, are of such large size that storing and processing their information has too high a cost. As a result, one possible solution is to compress the graphs by merging nodes into supernodes. This study introduces a genetic algorithm for graph compression that is based on the similarity of nodes, where two nodes are considered similar if a high proportion of their neighbours are in common. The methodology was applied to three real-world graphs storing widely varying data, as well as the gene regulatory network of E. coli. This study used a fixed compression rate of 25 % as a target for the graphs. Results for a parameter study of variation operators exhibit a strong preference for crossover, in comparison to mutation which was found to be disruptive to graph structure.

关键词： Escherichia coli

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A Novel Biologically Inspired Hierarchical Model for Image Recommendation 14th

A Novel Biologically Inspired Hierarchical Model for Image R...

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14th International symposium on Neural Networks (ISNN)

作者： Lu, Yan-Feng Qiao, Hong Li, Yi Jia, Li-Hao Zhang, Ai-Xuan Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai Peoples R China Nanchang Univ Sch Informat Engn Nanchang Jiangxi Peoples R China North China Univ Technol Sch Mech & Mat Engn Beijing Peoples R China

ISBN: (纸本)9783319590813;9783319590806

The biologically inspired model (BIM) for invariant feature representation has attracted widespread attention recently, which approximately follows the organization of cortex visuel. BIM is a computational architecture with four layers. With the image data size increases, the four-layer framework is prone to be overfitting, which limits its application. To address this issue, motivated by biology, we propose a biologically inspired hierarchical model (BIHM) for image feature representation, which adds two more discriminative layers upon the conventional four-layer framework. In contrast to the conventional BIM that mimics the inferior temporal cortex, which corresponds to the low level feature invariance and selectivity, the proposed BIHM adds two more layers upon the conventional BIM framework to simulate inferotemporal cortex, exploring higher level feature invariance and selectivity. Furthermore, we firstly utilize the BIHM in the image recommendation. To demonstrate the effectiveness of proposed model, we use it in image recommendation task and perform experiment on CalTech5 datasets. The experiment results show that BIHM exhibits higher performance than conventional BIM and is very comparable to existing architectures.

关键词： Visual cortex Biologically inspired model Classification Image recommendation

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computational intelligence for Metabolic Pathway Design: Application to the Pentose Phosphate Pathway 13

Computational Intelligence for Metabolic Pathway Design: App...

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13th ieee Annual Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Skelton, D. J. Hallinan, J. S. Park, S. Wipat, A. Newcastle Univ Sch Comp Sci Newcastle Upon Tyne Tyne & Wear England Macquarie Univ Dept Biol Sci Sydney NSW Australia Newcastle Univ Ctr Bacterial Cell Biol Sch Comp Sci Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Ctr Hlth & Bioinformat Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781467394727

Metabolic engineering is increasingly being used for the production of industrial products such as pharmaceuticals and enzymes. These chemicals have traditionally been chemically synthesized, but the application of synthetic biology techniques to microbes facilitates faster, cheaper production. Modelling and the integration of existing data can help inform the design of synthetic pathways. We applied an evolutionary algorithm to a flux balance model of metabolism in the industrially important bacterium Bacillus subtilis. Our target metabolites are sedoheptulose- 7-phosphate and riboflavin, components of the pentose phosphate pathway. The algorithm combines the results of the flux balance analysis with phylogenetic information derived from data warehouses, to predict several potential interventions to the metabolic network, mostly involving knockouts of genes related to the pathway.

关键词： Evolutionary computation data integration metabolic engineering genomics synthetic biology

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computational Prediction of Bacterial Type IV-B Effectors Using C-terminal Signals and Machine Learning Algorithms 13

Computational Prediction of Bacterial Type IV-B Effectors Us...

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13th ieee Annual Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Zou, Lingyun Chen, Kang Third Mil Med Univ Coll Basic Med Sci Dept Microbiol Bioinformat Ctr Chongqing Peoples R China Third Mil Med Univ Southwest Hosp Dept Radiol Chongqing Peoples R China

ISBN: (纸本)9781467394727

Many species of bacteria inject effector proteins to host cells by their type IV secretion systems(T4SS). Two main kinds of T4SS subtypes, IVA and IVB, are well studied in recent years. IVB effectors have been confirmed to be involved in the pathogenicity of various human pathogens. Discriminating these proteins in bacterial genomes are very helpful for identifying their functional roles in hosts. However, there are few effective computational methods can achieve these goals. In this study, the C-terminal sequence features were analyzed, furthermore, a novel algorithm based on machine learning was developed to predict IVB effectors in genomic proteins. Tests on datasets showed that this method can discriminate IVB effectors from non-effectors with over 94.4% accuracy and 81.6% true positive rate. Genome-wide tests in Coxiella burnetii also showed this algorithm is highly sensitive to recognize effector proteins. As a whole, this method is very helpful for new IVB effector identification and other relevant biological studies.

关键词： type IVB effector computational prediction machine learning feature calculation

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computational Tools for Analysis of Codon Usage in Viral Genomes and its Hosts

Computational Tools for Analysis of Codon Usage in Viral Gen...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Jonathan Felipe Xavier Catherine Putonti Cristiane Neri Nobre Pontificia Universidade Catolica de Minas Gerais Belo Horizonte MG BR Department of Biology and Computer Science Loyola University of Chicago Chicago Illinois EUA Department of Computer Science Pontifical Catholic University of Minas Gerais Belo Horizonte Minas Gerais Brazil

Analysis of codon selection in viral genomes is an important step for comprehension of the coevolution of a virus and its hosts. In this work, we developed a computational tool to simplify the analyses of codon usage. This tool, CUV or the Codon Usage Viewer, allows users to calculate and compare codon usage between viral and bacterial species including both graphical visualization and statistical analyses. To test our tool, the codon usage of homologous genes from the bacterial virus species PhiX174, G4 e alpha3, all belonging to the Microviridae family, were examined. The viruses codon usage was then compared to that of its common host, Escherichia coli. The results showed a conservation between the species codon usage.

关键词： Tools Amino acids Genomics Microorganisms Viruses (medical) Proteins

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Protein Loop Modeling Using Deep Generative Adversarial Network

Protein Loop Modeling Using Deep Generative Adversarial Netw...

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International Conference on Tools for Artificial intelligence (ICTAI)

作者： Zhaoyu Li Son P. Nguyen Dong Xu Yi Shang Department of Electrical Engineering and Computer Science University of Missouri Columbia MO USA

biology and medicine have a long-standing interest in computational structure prediction and modeling of proteins. There are often missing regions or regions that need to be remodeled in protein structures. The process of predicting particular missing regions in a protein structure is called loop modeling. In this paper, we propose a generative adversarial network (GAN) in deep learning for loop modeling using the idea of image inpainting. The generative network is to capture the context of the loop region and predict the missing area. The adversarial network is to make the prediction look real and provide gradients to the generative network. The proposed network was evaluated on a common benchmark for loop modeling. Experiments show that our method can successfully predict the loop region and has achieved better performance than the state-of-the-art tools. To our knowledge, this work represents the first attempt of using GAN for any bioinformatics studies.

关键词： Proteins Neural networks Predictive models Tools computational modeling Gallium nitride Machine learning

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Whole Genome Phylogenetic Tree Reconstruction Using Colored de Bruijn Graphs

Whole Genome Phylogenetic Tree Reconstruction Using Colored ...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Cole A. Lyman M. Stanley Fujimoto Anton Suvorov Paul M. Bodily Quinn Snell Keith A. Crandall Seth M. Bybee Mark J. Clement Computer Science Department Brigham Young University Provo Utah USA Brigham Young University Provo UT US Department of Biology Brigham Young University Provo Utah USA Computational Biology Institute George Washington University Washington DC USA

We present kleuren, a novel assembly-free method to reconstruct phylogenetic trees using the Colored de Bruijn Graph. kleuren works by constructing the Colored de Bruijn Graph and then traversing it, finding bubble structures in the graph that provide phylogenetic signal. The bubbles are then aligned and concatenated to form a supermatrix, from which a phylogenetic tree is inferred. We introduce the algorithms that kleuren uses to accomplish this task, and show its performance on reconstructing the phylogenetic tree of 12 Drosophila species. kleuren reconstructed the established phylogenetic tree accurately, and is a viable tool for phylogenetic tree reconstruction using whole genome sequences. Software package available at: https://***/Colelyman/kleuren.

关键词： Phylogeny Color Image color analysis bioinformatics Genomics Vegetation Software packages

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FAST: Fast Annotation with Synchronized Threads 13

FAST: Fast Annotation with Synchronized Threads

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13th ieee Annual Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Kim, Dongjae Hahn, Aria S. Hanson, Niels W. Konwar, Kishori M. Hallam, Steven J. Univ British Columbia Dept Microbiol & Immunol Vancouver BC Canada Koonkie Inc Menlo Pk CA 94025 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Univ British Columbia ECOSCOPE Training Program Vancouver BC Canada Univ British Columbia Dept Comp Sci Vancouver BC Canada Univ British Columbia Grad Program Bioinformat Vancouver BC Canada KPMG LLC Data & Analyt Seattle WA USA

ISBN: (纸本)9781467394727

FAST is a multi-threaded, I/O optimized Seed-and-Extend alignment program. FAST is extensible to nucleotide sequences making it comparable to both BLASTn and BLASTp, and also features several new usage flags reporting only HSPs meeting user defined e-value cut-offs. FASTs threaded database construction allows fast, low memory database construction e.g., RefSeq (9.4GB) can be indexed in under 5 minutes using 20 threads. The threaded database construction gives users the ability to create custom databases on demand which is useful for tasks requiring self-alignment, such as network construction [30]. Finally, FAST supports incremental database construction enabling users to keep their databases up-to-date by adding sequences without suffering the cost of reformatting the entire database. The FAST source is freely available through GitHub https:// ***/hallamlab/FAST and test datasets can be found on Dropbox https://***/sh/xvvavweuzgqybc4/ AAAWCrRnol67ZsXi2qLQWUuOa? © 2016 ieee.

关键词： Database systems

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2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

2010 IEEE Symposium on Computational Intelligence in Bioinfo...

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2010 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

ISBN: (纸本)9781424467662

The proceedings contain 42 papers. The topics discussed include: supervised learning of maternal cigarette-smoking signatures from placental gene expression data: a case study;exploring structural modeling of proteins for kernel-based enzyme discrimination;towards a temporal modeling of the genetic network controlling systemic acquired resistance in arabidopsis thaliana;DGA: decomposition with genetic algorithm for multiple sequence alignment;integrating multiple scoring functions to improve protein loop structure conformation space sampling;classification of HIV-1 protease crystal structures using random forest, linear discriminant analysis and logistic regression;a fitness-independent evolvability measure for evolutionary developmental systems;and identification of a gene expression core signature for Duchenne muscular dystrophy (DMD) via integrative analysis reveals novel potential compounds for treatment.

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The Relevance of Upstream and Downstream Regions of mRNA in the Prediction of Translation Initiation Site of the Protein

The Relevance of Upstream and Downstream Regions of mRNA in ...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Wallison W. Guimarães Cristiano L. N. Pinto Cristiane N. Nobre Luis E. Zárate Pontificia Universidade Catolica de Minas Gerais Belo Horizonte MG BR School of Engineering of Minas Gerais Brazil Pontifical Catholic University of Minas Gerais Brazil

The correct prediction of Protein Translation Initiation Site is a problem of considerable interest in the molecular biology. In the literature, some papers investigate conservative positions of the mRNA that favor an ideal context for begin of translation, as it is the case of the work of Kozak. The Kozak Consensus is known to reveal some important mRNA upstream region positions to recognize an early translation. This work investigated the real contribution of these conservative positions, besides analyzing sequences in the downstream region to be considered the non-initiators of translation. In this paper, we use the Support Vector Machine classifier and three different scenarios. The results indicate that the proposed methodology was efficient, guaranteeing a gain in the performance of the larger organisms. For the smaller organisms, the three scenarios investigated maintain the same behavior. However, due to the lack of consideration of the upstream region of the mRNA, the proposed methodology allows evaluating organisms not considered in other works, as is the case of the Caenorhabditis elegans organism.

关键词： Organisms Proteins Databases Support vector machines Training computational modeling bioinformatics

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