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Guest Editorial for the 13th Asia Pacific bioinformatics conference

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2015年第6期12卷 1217-1218页

作者： Huang, Hsien-Da Chen, Yi-Ping Phoebe Natl Chiao Tung Univ Inst Bioinformat & Syst Biol Hsinchu Taiwan La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia

Presents papers that were presented at the 13th Asia Pacific bioinformatics conference.

关键词： Special issues and sections Meetings bioinformatics

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Classification of Parkinson's Disease Patients and Effectiveness of Medication for Freezing of Gait

Classification of Parkinson's Disease Patients and Effective...

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ieee conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Beigi, Omid Mohamad Nobrega, Ligia Reis Houghten, Sheridan Andrade, Adriano de Oliveira Pereira, Adriano Alves Brock Univ Dept Comp Sci St Catharines ON Canada Univ Fed Uberlandia Fac Elect Engn Uberlandia MG Brazil

ISBN: (纸本)9781665484626

Parkinson's disease (PD), a neurodegenerative disease with symptoms hard to distinguish from other disorders, affects millions of people globally. Among the symptoms of PD, changes in gait have been used as a primary diagnosis factor. Symptoms of PD include bradykinesia, tremors, depression, hallucinations, cognitive decline, and falls. This study presents a dataset that records data on PD patients who experience freezing of gait, including data for medication in the "on" and "off" states. Classification is applied to two problems relating to this PD data: first, to distinguish PD patients from healthy individuals, and second, to determine effectiveness of medication for freezing of gait. Among the classifiers considered, Multilayer Perceptron, K-Nearest Neighbors, Random Forest, and Support Vector Machine obtain the best results when applied to these problems.

关键词： Support vector machines Parkinson's disease Multilayer perceptrons Depression bioinformatics Medical diagnostic imaging computational intelligence

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Guest Editorial for Special Section on the 12th International conference on Intelligent Computing (ICIC)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第5期15卷 1433-1435页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Machine Learning & Syst Biol Lab Shanghai 201804 Peoples R China Polytech Bari Elect & Informat Engn Dept I-70126 Bari BA Italy Indian Inst Technol IIT Madras Dept Biotechnol Madras 600036 Tamil Nadu India

The eight papers included in this special section were presented at the 12th International conference on Intelligent Computing (ICIC) held at Lanzhou, China, during August 2-5, 2016. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Meetings bioinformatics Machine learning Intelligent systems Machine Learning Intelligent systems international conference bioinformatics

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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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Special section on machine intelligence approaches systems biology

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ieee TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2008年第1期38卷 2-4页

作者： Fogel, Gary B. Bullinger, Eric Su, Shun-Feng Azuaje, Francisco Nat Select Inc San Diego CA 92130 USA Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10617 Taiwan Univ Ulster Jordanstown BT37 0QB North Ireland

The three papers in this special section focus on machine intelligence approaches to systems biology.

关键词： Special issues and sections Machine intelligence Systems biology bioinformatics Proteins computational intelligence Biological system modeling Electrical engineering Biological systems biology computing

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DNA Region Grammatical Inference

DNA Region Grammatical Inference

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ieee International conference on bioinformatics and Biomedicine (ieee BIBM)

作者： Cook, Cory San Jose State Univ Comp Sci San Jose CA 95192 USA

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses p... 详细信息

ISBN: (纸本)9781509016105

关键词： DNA computational biology field computational biology bioinformatics DNA language definition statistical information

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Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: beyond edge weights in psychological networks

Structural and parametric uncertainties in full Bayesian and...

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ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Hullam, Gabor Juhasz, Gabriella Deakin, Bill Antal, Peter Budapest Univ Technol & Econ Dept Measurement & Informat Syst Budapest Hungary Hungarian Acad Sci MTA SE Neuropsychopharmacol & Neurochem Res Grp Budapest Hungary Semmelweis Univ Dept Pharmacodynam Fac Pharm Budapest Hungary Univ Manchester Fac Med & Human Sci Sch Community Based Med Neurosci & Psychiat Unit Manchester Lancs England

ISBN: (纸本)9781467389884

Uncertainty over model structures poses a challenge for many approaches exploring effect strength parameters at system-level. Monte Carlo methods for full Bayesian model averaging over model structures require considerable computational resources, whereas bootstrapped graphical lasso and its approximations offer scalable alternatives with lower complexity. Although the computational efficiency of graphical lasso based approaches has prompted growing number of applications, the restrictive assumptions of this approach are frequently ignored. We demonstrate using an artificial and a real-world example that full Bayesian averaging using Bayesian networks provides detailed estimates through posterior distributions for structural and parametric uncertainties and it is a feasible alternative, which is routinely applicable in mid-sized biomedical problems with hundreds of variables. We compare Bayesian estimates with corresponding frequentist quantities from bootstrapped graphical lasso using pairwise Markov Random Fields, discussing also their different interpretations. We present results using synthetic data from an artificial model and using the UK Biobank data set to construct a psychopathological network centered around depression (this research has been conducted using the UK Biobank Resource under Application Number 1602).

关键词： Bayes methods computational modeling Data models Analytical models Markov random fields Uncertainty

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A swarm intelligence based algorithm for proteomic pattern detection of ovarian cancer

A swarm intelligence based algorithm for proteomic pattern d...

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ieee Symposium on computational intelligence in bioinformatics and computational biology

作者： Meng, Yan Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)1424406234

The advanced protein profiling technologies can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establish signature proteomic patterns that distinguish cancer from non-cancer. It is desirable to have complex and intelligent analytical tools to detect the changes in protein expression and their correlation to diseases conditions. This paper proposed a swarm-agent based intelligence algorithm using a hybrid ant colony optimization/particle swarm optimization (ACO/PSO) algorithm to identify the diagnostic proteomic patterns of biomarkers for early detection of ovarian cancer. The experimental results demonstrated that the proposed system has high predictive accuracy and better diagnostic performance.

关键词： swarm agents ant colony optimization particle swarm optintization proteomic pattern detection SELDI-TOF profiling

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computational intelligence Tools and Databases in bioinformatics 4

Computational Intelligence Tools and Databases in Bioinforma...

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4th ieee International conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Alansari, Zainab Anuar, Nor Badrul Kamsin, Amirrudin Soomro, Safeeullah Belgaum, Mohammad Riyaz Univ Malaya Dept Comp Sci & Informat Technol Kuala Lumpur Malaysia AMA Int Univ Dept Comp Studies Salmabad Bahrain

ISBN: (纸本)9781538621073;9781538621066

bioinformatics is overgrowing and has found many new applications which its progress caused the interaction with other fields. Thus presently, the computational intelligence based on Internet of Things in bioinformatics is used to archive, search, display, analysis and interpret biological data. Development of bioinformatics and growing need for actuarial science, computer, and programming result in the growth of new computational intelligence methods, tools, computer algorithms, and programming solutions. This study confers an extensive survey of the computational intelligence (CI) methods based on Big Data and IoT in bioinformatics and reviews some tools and databases of computational intelligence for bioinformatics issues such as DNA Sequence Analysis Tools, Analyze & Model 3D Structure, Phylogenetic Analysis, Protein Function Assignment, and Protein Sequence & Structure.

关键词： computational intelligence bioinformatics Big Data Machine Learning

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Data Mining Over Biological Datasets: An Integrated Approach Based on computational intelligence

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ieee computational intelligence MAGAZINE 2012年第4期7卷 22-34页

作者： Stegmayer, Georgina Gerard, Matias Milone, Diego H. Natl Sci & Tech Res Council CONICET Ctr Res & Dev Informat Syst CIDISI Buenos Aires DF Argentina Natl Sci & Tech Res Council CONICET Res Ctr Signals Syst & Computat Intelligence Sinc FICH UNL Buenos Aires DF Argentina

biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations;a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters;a validation measure that can assess the biological significance of the clusters found;and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.

关键词： Data mining Medical information processing Genomics bioinformatics Knowledge discovery Informatics Clustering algorithms biology Evolutionary computation

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