Cerberus: A New Information Retrieval Tool for Marine Metagenomics

• Autorzy: Radom M. , Kottmann R. , Rybarczyk A. , Formanowicz P. , Szachniuk M. , Glöckner F. O. , Błażewicz J.
• Języki publikacji: EN

Abstrakty

EN

The number of papers published every year in scientific journals is growing tremendously, especially in biological sciences. Keeping the track of a given branch of science is therefore a difficult task. This was one of the reasons for developing the classification tool we called Cerberus. The classification categories may correspond to some areas of research defined by the user. We have used the tool to classify papers as containing marine metagenomic, terrestrial metagenomic or non-metagenomic information. Cerberus is based on special filters using weighted domain vocabularies. Depending on the number of occurrences of the keywords from the vocabularies in the paper, the program classifies the paper to a predefined category. This classification can precede the information extraction since it can reduce the number of papers to be analyzed. Classification of papers using the method we propose results in an accurate and precise result set of articles that are relevant to the scientist. This can reduce the resources needed to find the data required in ones field of studies.

Słowa kluczowe

EN

metagenomics; information retrieval; information extraction

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2010
• Tom: Vol. 35, No. 2
• Strony: 107--126
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Radom M.

autor

Kottmann R.

autor

Rybarczyk A.

autor

Formanowicz P.

autor

Szachniuk M.

autor

Glöckner F. O.

autor

Błażewicz J.

• Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Bibliografia

• [1] Cavicchioli, R., Demaere, M.Z., Thomas,T., Metagenomic studies reveal the critical and wide-ranging ecological importance of uncultivated Archaea: the role of ammonia oxidizers, BioEssays, 29, 2007, 11-14.
• [2] Cohen, A.M., Hersh, W., A survey of current work in biomedical text mining, Briefings in Bio in formatics, 6, 2005, 57-71
• [3] Eibe, F., Mark, A., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I.H., Trigg, L., Weka - a machine learning workbench for data mining, The Data Mining and Knowledge Discovery Handbook, Springer, 2005, 1305-1314.
• [4] Google Scholar, [http://scholar.google.com]
• [5] Jensen, L.J. Jasmin, S., Peer, B., Literature mining for the biologist: from information retrieval to biological discoveiy, Nature Reviews Genetics, 7, 2006, 119-129.
• [6] Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, Improvements to Piatt's SMO Algorithm for SVM Classifier Design, Neural Computation, 13, 3, 2001, 637-649.
• [7] Krallinger, M., Valencia, A., Text-mining and information-retrieval services for molecular biology, Genome Biology, 6, 2005, 224.
• [8] Lombardot, T., Kottmann, R., Pfeffer, H., Richter, M., Teeling, H., Quast, C., Glöckner, F.O., Megx.net - database resources for marine ecological genomics, Nucleic Acids Res., 34, 1, 2006, 390-393.
• [9] MapServer,GeneveGRID, [http://www.grid.unep.ch/activities/assessment/metafunctions/index.php]
• [10] MetaFunctions [http://www.metafunctions.org/]
• [11] Muller, H.M., Kenny, E.E., Sternberg, P.W., Textpresso: an ontology-based information retrieval and extraction system for biological literature, PLoS Biol, 2, 2004, 1984-1998.
• [12] Platt, J., CCSP Group, Microsoft Research, Advances in Kernel Methods - Support Vector Learning, MIT Press, 1999, 185-208.
• [13] Riesenfeld, C.S., Schloss, P.D, Handelsman, J., Metagenomics: Genomic Analysis of Microbial Communities, Annual Review of Genetics, 38, 2004, 525-552.
• [14] Salton, G., McGill, M.J., Introduction to modern information retrieval McGraw-Hill, 1983, ISBN 0070544840.
• [15] Salton, G., Buckley, C., Term-weighting approaches in automatic text retrieval, Information Processing & Management, 24, 1988, 513-523.
• [16] Schuler, G., Epstein, J., Ohkawa, H., Kans J., Entrez: molecular biology database and retrieval system, Methods Enzymol, 266, 1996, 141-162.
• [17] Tanabe, L. Scherf U., Smith L.H., Lee J.K., Hunter L., Weinstein J.N., MedMiner: An internet text-mining tool for biomedical information, with application to gene expression profiling, Biotechniques, 27, 1999, 1210-1217.
• [18] Venter, C., Remington, K., Heidelberg, F., Environmental genome shotgun sequencing of the Sargasso Sea, Science, 304, 2004, 66-74.