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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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International conference on Practical Applications of computational Biology and bioinformatics PACBB, 2016

International Conference on Practical Applications of Comput...

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International conference on Practical Applications of computational Biology and bioinformatics PACBB, 2016

ISBN: (纸本)9783319401256

The proceedings contain 22 papers. The special focus in this conference is on Data and Text Mining. The topics include: Intelligent systems for predictive modelling in cheminformatics;a challenge for deep learning;visual exploratory assessment of class C GPCR extracellular domains discrimination capabilities;development of a machine learning framework for biomedical text mining;sequence retriever for known, discovered, and user-specified molecular fragments;sensitivity, specificity and prioritization of gene set analysis when applying different ranking metrics;deep data analysis of a large microarray collection for leukemia biomarker identification;systematic evaluation of gene expression data analysis methods using benchmark data;a clustering-based method for gene selection to classify tissue samples in lung cancer;large-scale transcriptomic approaches for characterization of post-transcriptional control of gene expression;ultra-fast fuzzy whole genome alignment;exploring the high performance computing-enablement of a suite of gene-knockout based genetic engineering applications;an application that executes parallelized pipelines to analyse next-generation sequencing data;exceptional symmetry profile;a computation tool for the estimation of biomass composition from genomic and transcriptomic information;role of nerve growth factor signaling in cancer cell proliferation and survival using a reachability analysis approach;a hybrid of harmony search and minimization of metabolic adjustment for optimization of succinic acid production;a web-based application for network reconstruction and visualization of cytometry data and reconstruction of metabolic models for liver cancer cells.

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First Trimester Noninvasive Prenatal Diagnosis: A computational Intelligence Approach

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ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2016年第5期20卷 1427-1427页

作者： Neocleous, Andreas C. Nicolaides, Kypros H. Schizas, Christos N. Univ Groningen Johann Bernoulli Inst Math & Comp Sci Dept Intelligent Syst Grp NL-9712 CP Groningen Netherlands Univ Cyprus Dept Comp Sci CY-2109 Nicosia Cyprus Kings Coll Hosp London Harris Birthright Res Ctr Fetal Med London SE5 9RS England

The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.), from suitable sonographic, biochemical markers, and other relevant data. A database(1) consisted of 51,208 singleton pregnancy cases, while undergoing first trimester screening for aneuploidies has been used for the building, training, and verification of the proposed method. From all the data collected for each case from the mother and the fetus, the following 9 are considered by the collaborating obstetricians as the most relevant to the problem in question: maternal age, previous pregnancy with T21, fetal crown-rump length, serum free beta-hCG in multiples of the median (MoM), pregnancy-associated plasma protein-A in MoM, nuchal translucency thickness, nasal bone, tricuspid flow, and ductus venosus flow. The dataset was randomly divided into a training set that was used to guide the development of various ANN schemes, support vector machines, and k-nearest neighbor models. An evaluation set used to determine the performance of the developed systems. The evaluation set, totally unknown to the proposed system, contained 16,898 cases of euploidy fetuses, 129 cases of T21, and 76 cases of O.C.A. The best results were obtained by the ANN system, which identified correctly all T21 cases, i.e., 0% false negative rate (FNR) and 96.1% of euploidies, i.e., 3.9% false positive rate (FPR), meaning that no child would have been born with T21 if only that 3.9% of all pregnancies had been sent for invasive testing. The aim of this work is to produce a practical tool for the obstetrician which will ideally provide 0% FNR and to recommend the minimum possible number of cases for further testing such as invasive. In conclusion, it was demonstrated that ANN schemes can provide an effective ear

关键词： bioinformatics chromosomal abnormalities computational intelligence non-invasive prenatal diagnosis

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A new phylogenetic tree sampling method for maximum parsimony bootstrapping and proof-of-concept implementation 8

A new phylogenetic tree sampling method for maximum parsimon...

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8th International conference on Knowledge and systems Engineering, KSE 2016

作者： Hoang, Diep Thi Vinh, Le Sy Flouri, Tomas Stamatakis, Alexandros Von Haeseler, Arndt Minh, Bui Quang Faculty of Information Technology University of Engineering and Technology VNU Hanoi Viet Nam Heidelberg Institute for Theoretical Studies Institute for Theoretical Informatics Karlsruhe Germany Karlsruhe Institute of Technology Institute for Theoretical Informatics Karlsruhe Germany Center for Integrative Bioinformatics Vienna Max F. Perutz Laboratories University of Vienna Medical University Vienna Austria Bioinformatics and Computational Biology Faculty of Computer Science University of Vienna Vienna Austria

ISBN: (纸本)9781467389297

Reconstructing maximum parsimony (MP) trees along with bootstrap for large multiple sequence alignments is a computationally challenging task. In this paper, we present MPBoot, a new method for approximating MP bootstrap based on the idea of a previously introduced method for maximum likelihood bootstrap. MPBoot economizes the computations by only sampling trees on the original alignment and simultaneously calculating exact parsimony scores for all bootstrap alignments. Preliminary experiments on simulated data showed that MPBoot is about 2-3.5 times faster and yields better parsimony scores than a fast bootstrap procedure implemented in TNT. MPBoot also produced less biased support value than TNT for small simulated data sets (=500) MPBoot overestimates the probability that a clade is true, i.e., results in likely high false positive rates. As MPBoot is currently a proof-of-concept implementation, more work is to understand the limitations of the method. © 2016 ieee.

关键词： Maximum likelihood

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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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DeepEnhancer: Predicting enhancers by convolutional neural networks

DeepEnhancer: Predicting enhancers by convolutional neural n...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Xu Min Ning Chen Ting Chen Rui Jiang Department of Computer Science and Technology State Key Lab of Intelligent Technology and Systems Bioinformatics Division Center for Synthetic & Systems Biology Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781509016129

Enhancers are crucial to the understanding of mechanisms underlying gene transcriptional regulation. Although having been successfully applied in such projects as ENCODE and Roadmap to generate landscape of enhancers in human cell lines, high-throughput biological experimental techniques are still costly and time consuming for even larger scale identification of enhancers across a variety of tissues under different disease status, making computational identification of enhancers indispensable. In this paper, we propose a computational framework, named DeepEnhancer, to classify enhancers from background genomic sequences. We construct convolutional neural networks of various architectures and compare the classification performance with traditional sequence-based classifiers. We first train the deep learning model on the FANTOM5 permissive enhancer dataset, and then fine-tune the model on ENCODE cell type-specific enhancer datasets by adopting the transfer learning strategy. Experimental results demonstrate that DeepEnhancer has superior efficiency and effectiveness in classification tasks, and the use of max-pooling and batch normalization is beneficial to higher accuracy. To make our approach more understandable, we propose a strategy to visualize the convolutional kernels as sequence logos and compare them against the JASPAR database using TOMTOM. In summary, DeepEnhancer allows researchers to train highly accurate deep models and will be broadly applicable in computational biology.

关键词： Computer architecture bioinformatics Kernel DNA Genomics Machine learning 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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Guest Editorial for Special Section on ISB/TBC 2014

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ieee-ACM TRANSACTIONS ON computational BIOLOGY AND bioinformatics 2015年第5期12卷 1074-1075页

作者： Zhang, Shihua Chen, Luonan Chinese Acad Sci Natl Ctr Math & Interdisciplinary Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Inst Biochem & Cell Biol Key Lab Syst Biol Innovat Ctr Cell Signaling Network Shanghai 200031 Peoples R China

The five papers in this special section were presented at the Eighth International conference on computational systems Biology and Fourth Translational bioinformatics conference (ISB/TBC 2014), which was held in Qingd... 详细信息

关键词： Special Issues And Sections bioinformatics Informatics Meetings

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2014 ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2014

2014 IEEE Conference on Computational Intelligence in Bioinf...

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9th ieee International conference on Distributed Computing in Sensor systems, DCOSS 2014

ISBN: (纸本)9781479946198

The proceedings contain 55 papers. The topics discussed include: low-power listening goes multi-channel;PhyTraces: simulating new RF environments with physical layer traces;a novel wake-up receiver with addressing capability for wireless sensor nodes;symmetric coherent link degree, adaptive throughput-transmission power for wireless sensor networks;indoor occupant positioning system using active RFID deployment and particle filters;harnessing non-uniform transmit power levels for improved sequence based localization;FlashTrack: a fast, in-network tracking system for sensor networks;crowd-sensing with polarized sources;a dual-sensor enabled indoor localization system with Crowdsensing spot survey;on decentralized in-network aggregation in real-world scenarios with crowd mobility;and the information funnel: exploiting named data for information-maximizing data collection.

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Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

Finding Influential Genes Using Gene Expression Data and Boo...

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Takeyuki Tamura Tatsuya Akutsu Chun-Yu Lin Jinn-Moon Yang Bioinformatics Center Kyoto University Uji Kyoto Japan Institute of Bioinformatics and Systems Biology National Chiao Tung University Hsinchu Taiwan

Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.

关键词： Biochemistry Gene expression Compounds Cancer bioinformatics Mathematical model

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