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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： D. J. Skelton J. S. Hallinan S. Park A. Wipat School of Computing Science Newcastle University Newcastle upon Tyne UK Department of Biological Sciences Macquarie University Sydney Australia

ISBN: (纸本)9781509000128

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： Biological system modeling Biochemistry Production Gold Data models Mathematical model Organisms

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Discovering Condition Specific Topological Pattern Changes in Coexpression Network: An Application to HIV-1 Progression

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2016年第6期13卷 1086-1099页

作者： Ray, Sumanta Bandyopadhyay, Sanghamitra Aliah Univ Dept Comp Sci & Engn Kolkata 700016 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

The natural progression of HIV-1 begins with a short acute retroviral syndrome which typically transit to chronic and clinical latency stages and subsequently progresses to a symptomatic, life-threatening immunodeficiency disease known as AIDS. Microarray analysis based on gene coexpression is widely used to investigate the coregulation pattern of a group (or cluster) of genes in a specific phenotype. Moreover, an investigation on the topological patterns across multiple phenotypes can facilitate the understanding of stage specific infection pattern of HIV-1 virus. Here, we develop a novel framework to identify topological patterns of gene co-expression network and detect changes of modular structure across different stages of HIV progression. This is achieved by comparing the topological and intramodular properties of HIV infection modules. To capture the diversity in modular structure, some topological, correlation based, and eigengene based measures are utilized here. We have applied a rank aggregation scheme to rank all the modules to provide a good agreement between these measures. Some novel transcription factors like 'FOXO1', 'GATA3', 'GFI1', 'IRF1', 'IRF7', 'MAX', 'STAT1', 'STAT3', 'XBP1', and 'YY1' that merge from the modules show significant change in expression pattern over HIV progression stages. Moreover, we have performed an eigengene based analysis to reveal the perturbation in modular structure across three stages of HIV-1 progression.

关键词： Gene coexpression rank aggregation transcription factor module eigengene HIV disease progression

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Evolving graph compression using similarity measures for bioinformatics applications

Evolving graph compression using similarity measures for bio...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Joseph Alexander Brown Sheridan Houghtent Tyler Kennedy Collins Qiang Qu Institute of Information Systems Innopolis University Innopolis Russia Brock University Saint Catharines ON CA Department of Computer Science Brock University Ontario Canada

ISBN: (纸本)9781509000128

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： Encyclopedias Electronic publishing Internet Evolutionary computation Physics

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A Hybrid of Harmony Search and Minimization of Metabolic Adjustment for Optimization of Succinic Acid Production

A Hybrid of Harmony Search and Minimization of Metabolic Adj...

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10th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Wahid, Nor Syahirah Abdul Mohamad, Mohd Saberi Salleh, Abdul Hakim Mohamed Deris, Safaai Chan, Weng Howe Omatu, Sigeru Manuel Corchado, Juan Sjaugi, Muhammad Farhan Ibrahim, Zuwairie Yusof, Zulkifli Md. Univ Teknol Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Locked Bag 01 Kota Baharu 16300 Kelantan Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Univ Salamanca BISITE Res Grp Biomed Res Inst Salamanca Salamanca Spain Perdana Univ Sch Data Sci Ctr Bioinformat Serdang 43400 Selangor Malaysia

ISBN: (纸本)9783319401263;9783319401256

Succinic acid has been favored by researchers due to its industrial multiuses. However, the production of succinic acid is far below cell theoretical maximum. The goal of this research is to identify the optimal set of gene knockouts for obtaining high production of succinic acid in microorganisms. Gene knockout is a widely used genetic engineering technique. Hence, a hybrid of Harmony Search (HS) and Minimization of Metabolic Adjustment (MOMA) is proposed. The dataset applied is a core Escherichia coli metabolic network model. Harmony Search is a meta-heuristic algorithm inspired by musicians' improvisation process. Minimization of Metabolic Adjustment is used to calculate fitness closest to the wild-type, after mutant gene knockout. The result obtained from the proposed hybrid technique are knockout genes list and production rate after the deletion. This proposed technique is possible to be applied in wet laboratory experiment to increase the production of succinic acid in E. coli.

关键词： bioinformatics Artificial intelligence Metabolic engineering Harmony Search Minimization of Metabolic Adjustment Gene knockout

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Risk factors rule mining in hypertension: Korean National Health and Nutrient Examinations Survey 2007–2014

Risk factors rule mining in hypertension: Korean National He...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Hyun Woo Park Erdenbileg Batbaatar Dingkun Li Keun Ho Ryu Database/Bioinformatics Laboratory Chungbuk National University South Korea

ISBN: (纸本)9781509000128

The prevention of hypertension is one of the most important topics in health research. In the most of the previous studies used statistical methods for analyzing the association between hypertension prevalence and dietary. However, statistical methods have some limitation which are, it is difficult to interpret variables interaction at a time. Thus we apply the data mining techniques for generation of prognosis factors based on association rule mining. In our experiment, we conducted Korean National Health and Nutrient Examination Survey (KNHANES) data from 2007 to 2014. We used to filter-based feature selection method for find prognosis factors and we generate the rules based on discovered risk factors of prognosis in hypertension. We evaluated discovered rules by support and confidence. In the results shows that, we can find useful rules for prognosis of hypertension. We expected to support medical decision making and easy to interpret prognosis of hypertension.

关键词： Hypertension Prognostics and health management Diseases Sociology Statistics Data mining Feature extraction

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computational prediction of bacterial type IV-B effectors using C-terminal signals and machine learning algorithms

Computational prediction of bacterial type IV-B effectors us...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Lingyun Zou Kang Chen Department of Microbiology College of Basic Medical Sciences Third Military Medical University Chongqing China Radiology Department Southwest Hospital Third Military Medical University Chongqing China

ISBN: (纸本)9781509000128

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： Proteins Hidden Markov models Amino acids Microorganisms Genomics Machine learning algorithms bioinformatics

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Integrated analysis of microRNA regulation of genes in HSIL

Integrated analysis of microRNA regulation of genes in HSIL

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Shikha Suman Ashutosh Mishra Anurag Kulshrestha Department of applied science Indian institute of information technology Allahabad India Bioinformatics Division National Bureau of Animal Genetic Resource Karnal India

ISBN: (纸本)9781509000128

Cervical cancer is one of the deadliest cancers in women worldwide. Cervical cancer is preceded by HSIL in 20% of the cases. microRNA (miRNA) regulate the expression of a number of genes, therefore targeting miRNA and mRNA dependencies may facilitate in determining the regulatory mechanisms and molecular signatures of HSIL. miRNA and mRNA expression profiles of HSIL were analyzed to construct a miRNA-protein interaction network. Hub genes and functional modules of the interaction network were identified. KEGG pathway and disease enrichment analysis were also performed on DEGs. A miRNA-protein interaction network with 129 nodes and 363 edges was constructed. Two miRNAs namely has_miR_203 and has_miR_142_5p were found to regulate most DEGs. Two significant modules with 10 nodes and 6 nodes were identified from the network respectively. CCND1, BIRC5, PCNA, MAD2LI and NEK2 were upregulated DEGs and hubs of the network. Moreover, these five genes were also present in the significant modules. These miRNA and five genes with high connectivity might have prospective diagnostic and therapeutic values, may be associated with oncogenicity of HSIL.

关键词： Cervical cancer Proteins Neoplasms Lesions bioinformatics

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Using Bayesian modeling on molecular fragments features for virtual screening

Using Bayesian modeling on molecular fragments features for ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： David Hoksza Petr Škoda Faculty of Mathematics and Physics Charles University Prague Czech Republic

Virtual screening enables to search large small-molecule compound libraries for active molecules with respect to given macromolecular target. In ligand-based virtual screening, this goal is achieved by utilizing information about fragments or patterns present in existing known active compounds. Typically, the patterns are encoded as fingerprints which are used to screen a database of candidate compounds. In this work, we introduce an approach which uses Bayesian inference to encode activity-related information. Unlike previous approaches, our method does not utilize simple fragments, but rather uses features of these fragments. For each molecule, we generate a set of molecular fragments and extract molecular features for each of them. Next, we remove correlated features and use the remaining ones to build a Bayes model of activity. To score a previously unseen molecule, the molecule's fragment feature vectors are passed to the model and a score is obtained as the aggregation of their probability scores. When screening a database, this score is used to rank the compounds database. We show on datasets with various levels of difficulty that using fragments features rather then fragments themselves results in improvement of retrieval rates with respect to the best state-of-the art molecular fingerprints.

关键词： Compounds Feature extraction Databases Atomic measurements Niobium Bayes methods computational modeling

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Governing the swarm: Controlling a bio-hybrid society of bees & robots with computational feedback loops

Governing the swarm: Controlling a bio-hybrid society of bee...

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ieee symposium Series on computational intelligence

作者： Martin Stefanec Martina Szopek Thomas Schmickl Rob Mills Artificial Life Lab of the Institute of Zoology Karl-Franzens University Graz Graz Austria BioISI - Biosystems & Integrative Sciences Institute University of Lisbon Lisbon Portugal

Bio-hybrid systems in which living organisms interact and self-organize with multi-robot systems are a novel approach in engineering and biology. We show here how a group of honeybees and robots can interact in collective decision making and how computer code that adds feedback loops to the system may affect the global system properties. This study contains a series of experiments with living honeybees and robots as well as a cellular-automaton inspired model that is simple, yet still in good agreement with the empirical findings presented here. Using this model, we explore the most likely candidates for local parameters in the proximate mechanisms of the animals, thus further the understanding of this natural system in a context that is relevant for such bio-hybrid manifestations. We identify positive feedback based on bee-to-bee collision and temperature as an important factor governing collective decision making and found the stopping probability after close-encounters among bees as a crucial local parameter. This study is the first step towards using computer and robotic technology to monitor and control complex animal societies like honeybees.

关键词： Honeybees Swarm robotics Swarm intelligence Bio-hybrid systems Complex systems Nonlinear behavior Cellular models Feedbacks Collective decision making

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A* fast and scalable high-throughput sequencing data error correction via oligomers

A* fast and scalable high-throughput sequencing data error c...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Franco Milicchio Iain E. Buchan Mattia C.F. Prosperi Dept. of Engineering Roma Tre University Rome Italy Centre for Health Informatics University of Manchester Manchester United Kingdom Dept. of Epidemiology University of Florida Gainesville Florida USA

ISBN: (纸本)9781509000128

Next-generation sequencing (NGS) technologies have superseded traditional Sanger sequencing approach in many experimental settings, given their tremendous yield and affordable cost. Nowadays it is possible to sequence any microbial organism or meta-genomic sample within hours, and to obtain a whole human genome in weeks. Nonetheless, NGS technologies are error-prone. Correcting errors is a challenge due to multiple factors, including the data sizes, the machine-specific and non-at-random characteristics of errors, and the error distributions. Errors in NGS experiments can hamper the subsequent data analysis and inference. This work proposes an error correction method based on the de Bruijn graph that permits its execution on Gigabyte-sized data sets using normal desktop/laptop computers, ideal for genome sizes in the Megabase range, e.g. bacteria. The implementation makes extensive use of hashing techniques, and implements an A* algorithm for optimal error correction, minimizing the distance between an erroneous read and its possible replacement with the Needleman-Wunsch score. Our approach outperforms other popular methods both in terms of random access memory usage and computing times.

关键词： Error correction Genomics bioinformatics Sequential analysis Error analysis Organisms Computers

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