Cyfrifiadureg / Computer Science

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Recent Submissions

A Strategy for Selecting Data Mining Techniques in Metabolomics

BaniMustafa, Ahmed; Hardy, Nigel (Springer Science, February 29, 2012)

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http:/dx.doi.org/10.1007/978-1-61779-594-7_18

Abstract:

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

URI:

http://hdl.handle.net/2160/7821

Files in this item: 0

Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations (MIBBI) workshop

Kettner, Carsten; Field, Dawn; Sansone, Susanna; Taylor, Chris; Aerts, Jan; Binns, Nigel; Black, Andrew; Britten, Cedrik M; de Marco, Ario; Fostel, Jennifer; Gaudet, Pascale; Gonzalez-Beltran, Alejandra; Hardy, Nigel; Hellmans, Jan; Hermjakob, Hennin; Juty, Nick; Leebens-Mack, Jim; Maguire, Eamonn; Neumann, Steffen; Orchard, Sandra; Parkinson, Helen; Piel, William; Ranganathan, Shoba; Rocca-Serra, Philippe; Santarsiero, Annapaola; Shotton, David; Sterk, Peter; Untergasser, Andreas; Whetzel, Patricia L (Genomic Standards Consortium, December , 2010)

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http://dx.doi.org/10.4056/sigs.1473621

Abstract:	This report summarizes the proceedings of the second workshop of the ‘Minimum Information for Biological and Biomedical Investigations’ (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://mibbi.org/
Description:	Open Access article
URI:	http://hdl.handle.net/2160/7819

Files in this item: 1

147-3777-3-PB-1.pdf (222.7Kb)

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

Taylor, Chris F; Field, Dawn; Sansone, Susanna-Assunta; Aerts, Jan; Apweiler, Rolf; Ashburner, Michael; Binz, Pierre-Alain; Bogue, Molly; Booth, Tim; Brazma, Alvis; Brinkman, Ryan R; Ball, Catherine A; Deutsch, Eric W; Fiehn, Oliver; Fostel, Jennifer; Ghazal, Peter; Gibson, Frank; Clark, Adam Michael; Grimes, Graeme; Hancock, John M; Hardy, Nigel; Hermjakob, Henning; Julian, Randall K Jr; Gray, Tanya; Kettner, Carsten; Kinsinger, Christopher; Kolker, Eugene; Kuiper, Martin; Kane, Matthew; Leebens-Mack, Jim; Lewis, Suzanna E; Lord, Phillip; Mallon, Ann-Marie; Le Novère, Nicolas; Masuya, Hiroshi; McNally, Ruth; Mehrle, Alexander; Morrison, Norman; Marthandan, Nishanth; Quackenbush, John; Reecy, James M; Robertson, Donald G; Rocca-Serra, Philippe; Orchard, Sandra; Rosenfelder, Heiko; Santoyo-Lopez, Javier; Scheuermann, Richard H; Schober, Daniel; Rodriguez, Henry; Snape, Jason; Stoeckert, Christian J Jr; Tipton, Keith; Sterk, Peter; Untergasser, Andreas; Smith, Barry; Vandesompele, Jo; Wiemann, Stefan (Nature Publishing Group, August , 2008)

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http://dx.doi.org/doi:10.1038/nbt.1411

Abstract:	The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.
URI:	http://hdl.handle.net/2160/7818

Files in this item: 0

Face recognition using the POEM descriptor

Vu, Ngoc-Son; Dee, Hannah; Caplier, Alice (Elsevier, January 8, 2012)

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http://dx.doi.org/10.1016/j.patcog.2011.12.021

Abstract:

Real-world face recognition systems require careful balancing of three concerns: computational cost, robustness, and discriminative power. In this paper we describe a new descriptor, POEM (patterns of oriented edge magnitudes), by applying a self-similarity based structure on oriented magnitudes and prove that it addresses all three criteria. Experimental results on the FERET database show that POEM outperforms other descriptors when used with nearest neighbour classifiers. With the LFW database by combining POEM with GMMs and with multi-kernel SVMs, we achieve comparable results to the state of the art. Impressively, POEM is around 20 times faster than Gabor-based methods.

URI:

http://hdl.handle.net/2160/7802

Files in this item: 0

Building semantic scene models from unconstrained video

Dee, Hannah; Cohn, Anthony G; Hogg, David C (Elsevier, November 3, 2011)

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http://dx.doi.org/10.1016/j.cviu.2011.09.005

Abstract:

This paper describes a method for building semantic scene models from video data using observed motion. We do this through unsupervised clustering of simple yet novel motion descriptors, which provide a quantized representation of gross motion within scene regions. Using these we can characterise the dominant patterns of motion, and then group spatial regions based upon both proximity and local motion similarity to define areas or regions with particular motion characteristics. We are able to process scenes in which objects are difficult to detect and track due to variable frame-rate, video quality or occlusion, and we are able to identify regions which differ by usage but which do not differ by appearance (such as frequently used paths across open space). We demonstrate our method on 50 videos from very different scene types: indoor scenarios with unpredictable unconstrained motion, junction scenes, road and path scenes, and open squares or plazas. We show that these scenes can be clustered using our representation, and that the incorporation of learned spatial relations into the representation enables us to cluster more effectively. This method enables us to make meaningful statements about video scenes as a whole (such as “this video is like that video”) and about regions within these scenes (such as “this part of this scene is similar to that part of that scene”).

URI:

http://hdl.handle.net/2160/7801

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