Huang YF, Yeh HY, Soo VW (2013) Inferring drug-disease associations from integration of
chemical, genomic and phenotype data using network propagation. BMC Med Genomics 6
(3):1–14. doi:10.1186/1755-8794-6-S3-S4
Hubble J, Demeter J, Jin H et al (2009) Implementation of genepattern within the Stanford
microarray database. Nucleic Acids Res 37(1):D898–D901. doi:10.1093/nar/gkn786
Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P (2013) Computational drug
repositioning: from data to therapeutics. Clin Pharmacol Ther 93(4):335–341. doi:10.1038/
clpt.2013.1
Hwang T, Atluri G, Xie M, Dey S, Hong C, Kumar V, Kuang R (2012) Co-clustering
phenomegenome for phenotype classification and disease gene discovery. Nucleic Acids Res
40(19), e146. doi:10.1093/nar/gks615
Ito T, Tashiro K, Muta S et al (2000) Toward a protein-protein interaction map of the budding
yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations
between the yeast proteins. Proc Natl Acad Sci 97(3):1143–1147. doi:10.1073/pnas.97.3.1143
Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M,
Hirakawa M (2006) From genomics to chemical genomics: new developments in KEGG.
Nucleic Acids Res 34(Suppl 1):D354–D357. doi:10.1093/nar/gkj102
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and
interpretation of large-scale molecular data sets. Nucleic Acids Res 40(D1):D109–D114.
doi:10.1093/nar/gkr988
Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S,
Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A,
Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S,
Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA,
Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A (2009) Human protein
reference database 2009 update. Nucleic Acids Res 37(Suppl 1):D767–D772. doi:10.1093/nar/
gkn892
K€ohler S, Bauer S, Horn D, Robinson PN (2008) Walking the interactome for prioritization of
candidate disease genes. Am J Hum Genet 82(4):949–958. doi:10.1016/j.ajhg.2008.02.013
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems.
Computer 42(8):30–37. doi:10.1109/MC.2009.263
Krogan N, Cagney G, Yu H, Zhong G et al (2006) Global landscape of protein complexes in the
yeast saccharomyces cerevisiae. Nature 440:637–643. doi:10.1038/nature04670
Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture
phenotypic effects of drugs. Mol Syst Biol 6(1). doi:10.1038/msb.2009.98
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP,
Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ,
Clemons PA, Wei R, Carr SA, Lander ES, Golub TR (2006) The connectivity map: using gene-
expression signatures to connect small molecules, genes, and disease. Science 313
(5795):1929–1935. doi:10.1126/science.1132939
Lanckriet G, Deng M, Cristianini N, Jordan M, Noble W (2004) Kernel-based data fusion and its
application to protein function prediction in yeast. In: Biocomputing 2004, Proceedings of the
Pacific Symposium, Hawaii, USA pp 300–311, iSBN: 9812385983 Publisher: World Scientific
Name and Venue of Conference: Biocomputing 2004, Proceedings of the Pacific Symposium,
Hawaii, USA Other identifier: 2000790
Lanckriet GRG, De Bie T, Cristianini N et al (2004b) A statistical framework for genomic data
fusion. Bioinformatics 20(16):2626–2635. doi:10.1093/bioinformatics/bth294
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization.
Nature 401(6755):788–791
Lee I, Date SV, Adai AT, Marcotte EM (2004) A probabilistic functional network of yeast genes.
Science 306(5701):1555–1558. doi:10.1126/science.1099511
174 V. Gligorijevic ́and N. Pržulj