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Legal constraints on genetic data processing in European grids

10th HealthGrid Conference and the 4th International Workshop on science Gateways for Life sciences, IWSG-Life 2012

作者： Mouw, Evert Van't Noordende, Guido Van Kampen, Antoine H.C. Louter, Baas Santcroos, Mark Olabarriaga, Silvia D. Department of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Department of Medical Informatics Academic Medical Center University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Biosystems Data Analysis Swammerdam Institute for Life Science University of Amsterdam Netherlands

ISBN: (纸本)9781614990536

European laws on privacy and data security are not explicit about the storage and processing of genetic data. Especially whole-genome data is identifying and contains a lot of personal information. Is processing of such data allowed in computing grids? To find out, we looked at legal precedents in related fields, current literature, and interviews with legal experts. We found that processing of genetic data is only allowed on distributed systems with specific security measures, both technical and organizational. Informed consent, although important, offers no substitute for such requirements. © 2012 The authors and IOS Press. All rights reserved.

关键词： Digital storage

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On the integration of graph exploration and data analysis: The creative exploration toolkit

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2012年 7250卷 301-312页

作者： Haun, Stefan Gossen, Tatiana Nürnberger, Andreas Kötter, Tobias Thiel, Kilian Berthold, Michael R. Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany Department for Bioinformatics and Information Mining University of Konstanz Germany

ISBN: (纸本)9783642318290

To enable discovery in large, heterogenious information networks a tool is needed that allows exploration in changing graph structures and integrates advanced graph mining methods in an interactive visualization framework. We present the Creative Exploration Toolkit (CET), which consists of a state-of-the-art user interface for graph visualization designed towards explorative tasks and support tools for integration and communication with external data sources and mining tools, especially the data-mining platform KNIME. All parts of the interface can be customized to fit the requirements of special tasks, including the use of node type dependent icons, highlighting of nodes and clusters. Through an evaluation we have shown the applicability of CET for structure-based analysis tasks. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： User interfaces

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Integrated support for neuroscience research: From study design to publication

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10th HealthGrid Conference and the 4th International Workshop on science Gateways for Life sciences, IWSG-Life 2012

作者： Shahand, Shayan Caan, Matthan W.A. Van Kampen, Antoine H.C. Olabarriaga, Sílvia D. Department of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam PO Box 22700 1100 DE Amsterdam Netherlands Dept. of Radiology Brain Imaging Center University of Amsterdam Netherlands Biosystems Data Analysis Swammerdam Institute for Life Science University of Amsterdam Netherlands

ISBN: (纸本)9781614990536

Computational neuroscience is a new field of research in which neurodegenerative diseases are studied with the aid of new imaging techniques and computation facilities. Researchers with different expertise collaborate in these studies. A study requires scalable computational and storage capacity and information management facilities to succeed. Many virtual laboratories are proposed and developed to facilitate these studies, however most of them cover only the parts related to the computational data processing. In this paper we describe and analyse the phases of the computational neuroscience studies including the actors, the tasks they perform, and the characteristics of each phase. Based on these we identify the required properties and functionalities of a virtual laboratory that supports the actors and their tasks throughout the complete study. © 2012 The authors and IOS Press. All rights reserved.

关键词： Neurodegenerative diseases

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Whole Genome Gene Discovery in the Pathogenic Oomycete Pythium insidiosum by 454 Sequencing Technology

Whole Genome Gene Discovery in the Pathogenic Oomycete Pythi...

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The Annual Oomycete Molecular Genetics Network Meeting(国际卵菌分子遗传学年会)

作者： Theerapong Krajaejun Shoba Ranganathan Rommanee Khositnithikul Tassanee Lerksuthirat Tassanee Lowhnoo Thidarat Rujirawat Wanta Yingyong Prapat Suriyaphol Sittichoke Tangphatsornruang Gagan Garg Department of Pathology Mahidol University Bangkok Department of Chemistry and Biomolecular Sciences Macquarie University Australia Molecular Medicine Program Multidisciplinary Unit Faculty of Science Mahidol University Bangkok Research Center Faculty of Medicine Ramathibodi HospitalMahidol University Bangkok Bioinformatics and Data Management for Research Office for Research and Development Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Genome Sequencing Laboratory National Center for Genetic Engineering and Biotechnology National Science and Technology Development Agency PathumthaniThailand

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

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Proximity user identification using correlogram

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作者： Shahidi, Shervin Mazrooei, Parisa Esfahani, Navid Nasr Saraee, Mohammad Intelligent Databases Data Mining and Bioinformatics Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Isfahan Mathematics House Isfahan 81645-356 Iran School of Computing Science and Engineering University of Salford Greater Manchester United Kingdom

ISBN: (纸本)3642163262

This paper represents a technique, applying user action patterns in order to distinguish between users and identify them. In this method, users' actions sequences are mapped to numerical sequences and each user's profile is generated using autocorrelation values. Next, cross-correlation is used to compare user profiles with a test data. To evaluate our proposed method, a dataset known as Greenberg's dataset is used. The presented approach is succeeded to detect the correct user with as high as 82.3% accuracy over a set of 52 users. In comparison to the existing methods based on Hidden Markov Model or Neural Networks, our method needs less computation time and space. In addition, it has the ability of getting updated iteratively which is a main factor to facilitate transferability. © 2010 IFIP.

关键词： Pattern recognition

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CLASS: A general approach to classifying categorical sequences

CLASS: A general approach to classifying categorical sequenc...

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作者： Kelil, Abdellali Nordell-Markovits, Alexei Ousman Yassine Zaralahy, Parakh Wang, Shengrui ProspectUS Data Mining and Bioinformatics Laboratory Department of Computer Science Université de Sherbrooke Sherbrooke QC J1K 2R1 Canada

The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Signifi cant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches. © 2009 IEEE.

关键词： Semantics

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CLASS: a general approach to classifying categorical sequences

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Canadian Journal of Electrical and Computer Engineering 2009年第4期34卷 158-166页

作者： Abdellali Kelil Alexei Nordell-Markovits Parakh Ousman Yassine Zaralahy Shengrui Wang Prospect US Data Mining and Bioinformatics laboratory Department of Computer Science Université de Sherbrook Sherbrooke QUE Canada

The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Significant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches.

关键词： Proteins Matrix decomposition Indexes Training Semantics Testing

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Machine learning identification of zeolite framework types

Machine learning identification of zeolite framework types

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2009 International Conference on Artificial Intelligence, ICAI 2009

作者： Yang, Shujiang Lach-Hab, Mohammed Vaisman, Iosif I. Blaisten-Barojas, Estela Computational Materials Science Center George Mason University Fairfax VA United States Department of Bioinformatics and Computational Biology George Mason University Manassas VA United States Department of Computational and Data Sciences George Mason University Manassas VA United States

ISBN: (纸本)9781601321091

The characteristic framework types of zeolite crystals are routinely determined by calculating coordination sequences and vertex symbols of the 3D crystal structures. This method has limitations and tends to fail when the synthesized crystals are not close to perfect and present some types of crystallographic disorder. A machine learning based Zeolite-Structure-Predictor (ZSP) model is developed to predict framework types for both near perfect and moderately disordered zeolite crystals. The ZSP uses various attributes, including topological descriptors based on a computational geometry approach and relevant physical, chemical properties of the crystals. Trained with 41 framework types, the ZSP can correctly classify zeolite crystals with over 98% accuracy. Additionally, it is shown that the ZSP model is able to predict the framework types for strongly disordered zeolite crystals with reliable success rate.

关键词： Crystal structure

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Machine learning approach for classification of zeolite crystals

Machine learning approach for classification of zeolite crys...

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2008 International Conference on data Mining, DMIN 2008

作者： Yang, Shujiang Lach-Hab, Mohammed Vaisman, Iosif I. Blaisten-Barojas, Estela Computational Materials Science Center George Mason University MSN 6A2 Fairfax VA 22030 United States Department of Computational Biology and Bioinformatics George Mason University MSN 5B3 Manassas VA 20110 United States Department of Computational and Data Sciences George Mason University MSN 6A2 Fairfax VA 22030 United States

ISBN: (纸本)1601320620

A machine learning approach is applied to classify zeolite crystals according to their framework type. The Zeolite-Structure-Predictor is introduced based on the Random Forest algorithm. Zeolites structural data from the Inorganic Crystal Structure database (ICSD) are used to train the model. The ZSP uses sixteen attributes including topological descriptors obtained with statistical geometry and physical and chemical properties of individual zeolites. Trained with 40 framework types containing at least 5 instances per class, the ZSP can correctly classify zeolites with over 95% accuracy. The performance is shown to improve when more zeolite instances per class are available.

关键词： data mining

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