Biology Reference
In-Depth Information
Jensen, L. J. and Bork, P. (2010). Ontologies in quantitative biology: a basis for comparison,
integration, and discovery.
PLoS Biol.
8, e1000374.
Joshi, T., Chen, Y., Becker, J. M., Alexandrov, N., and Xu, D. (2005). Genome-scale gene
function prediction using multiple sources of high-throughput data in yeast
Saccharomy-
ces cerevisiae
.
OMICS J. Int. Biol.
8, 322-333.
Juck, D., Charles, T., Whyte, L. G., and Greer, C. W. (2000). Polyphasic microbial community
analysis of petroleum hydrocarbon-contaminated soils from two northern Canadian com-
munities.
FEMS Microbiol. Ecol.
33, 241-249.
Kao, K.-C. and Huang, J.-Y. (2010). Accurate and fast computational method for identifying
protein function using protein-protein interaction data.
Mol. Biosyst.
6, 830-839.
Kass, G. V. (1980). An exploratory technique for exploring large quantities of data.
Appl. Stat.
29, 119-127.
Keim, P., Price, L. B., Klevytska, A. M., Smith, K. L., Schupp, J. M., Okinaka, R.,
Jackson, P. J., and Hugh-Jones, M. E. (2000). Multiple-locus variable-number tandem
repeat analysis reveals genetic relationships within
Bacillus anthracis
.
J. Bacteriol.
182,
2928-2936.
Kennedy, R. L. (1997).
Solving Data Mining Problems Through Pattern Recognition.
Prentice
Hall PTR.
Kesselman, C. and Foster, I. (1998).
The Grid: Blueprint for a New Computing Infrastructure.
Morgan Kaufmann Publishers.
King, R. D., Karwath, A., Clare, A., and Dehaspe, L. (2000). Accurate prediction of protein
functional class from sequence in the
Mycobacterium tuberculosis
and
Escherichia coli
genomes using data mining.
Yeast
17, 283-293.
Kolluru, B., Nakjang, S., Hirt, R. P., Wipat, A., and Ananiadou, S. (2011). Automatic extrac-
tion of microorganisms and their habitats from free text using text mining workflows.
J. Integr. Bioinform.
8, 184-194.
Krause, L., Mchardy, A. C., Nattkemper, T. W., P¨hler, A., Stoye, J., and Meyer, F. (2007).
GISMO—gene identification using a support vector machine for ORF classification.
Nucleic Acids Res.
35, 540-549.
Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N.,
Tikuisis, A. P., Punna, T., Peregr´n-Alvarez, J. M., Shales, M., Zhang, X., Davey, M.,
Robinson, M. D., Paccanaro, A., Bray, J. E., Sheung, A., Beattie, B., Richards, D. P.,
Canadien, V., Lalev, A., Mena, F., Wong, P., Starostine, A., Canete, M. M.,
Vlasblom, J., Wu, S., Orsi, C., Collins, S. R., Chandran, S., Haw, R., Rilstone, J. J.,
Gandi, K., Thompson, N. J., Musso, G., St. Onge, P., Ghanny, S., Lam, M. H. Y.,
Butland, G., Altaf-Ul, A. M., Kanaya, S., Shilatifard, A., O'Shea, E., Weissman, J. S.,
Ingles, C. J., Hughes, T. R., Parkinson, J., Gerstein, M., Wodak, S. J., Emili, A., and
Greenblatt, J. F. (2006). Global landscape of protein complexes in the yeast
Saccharomy-
ces cerevisiae
.
Nature
440, 637-643.
Krogh,A. (1998). Chapter 4 an introduction tohiddenMarkovmodels for biological sequences. In
S. L. Salzberg, D. B. Searls & S. Kasif (Eds.),
New Comprehensive Biochemistry
.Elsevier.
Kumar, M., Verma, R., and Raghava, G. P. S. (2006). Prediction of mitochondrial proteins using
Support Vector Machine and Hidden Markov Model.
J. Biol. Chem.
281, 5357-5363.
Langille, M. G. I., Hsiao, W. W. L., and Brinkman, F. S. L. (2010). Detecting genomic islands
using bioinformatics approaches.
Nat. Rev. Microbiol.
8, 373-382.
Larsen, P. E., Field, D., and Gilbert, J. A. (2012). Predicting bacterial community assemblages
using an artificial neural network approach.
Nat. Methods
9, 621-625.
