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Items: 1 to 20 of 372


Predicting genetic interactions with random walks on biological networks.

Chipman KC, Singh AK.

BMC Bioinformatics. 2009 Jan 12;10:17. doi: 10.1186/1471-2105-10-17.


Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

Park C, Ahn J, Kim H, Park S.

PLoS One. 2014 Jan 31;9(1):e86309. doi: 10.1371/journal.pone.0086309. eCollection 2014.


Detecting disease genes based on semi-supervised learning and protein-protein interaction networks.

Nguyen TP, Ho TB.

Artif Intell Med. 2012 Jan;54(1):63-71. doi: 10.1016/j.artmed.2011.09.003. Epub 2011 Oct 14.


An integrative multi-network and multi-classifier approach to predict genetic interactions.

Pandey G, Zhang B, Chang AN, Myers CL, Zhu J, Kumar V, Schadt EE.

PLoS Comput Biol. 2010 Sep 9;6(9). pii: e1000928. doi: 10.1371/journal.pcbi.1000928.


Mining protein networks for synthetic genetic interactions.

Paladugu SR, Zhao S, Ray A, Raval A.

BMC Bioinformatics. 2008 Oct 9;9:426. doi: 10.1186/1471-2105-9-426.


Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins.

Qi Y, Tastan O, Carbonell JG, Klein-Seetharaman J, Weston J.

Bioinformatics. 2010 Sep 15;26(18):i645-52. doi: 10.1093/bioinformatics/btq394.


Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.

You ZH, Lei YK, Zhu L, Xia J, Wang B.

BMC Bioinformatics. 2013;14 Suppl 8:S10. doi: 10.1186/1471-2105-14-S8-S10. Epub 2013 May 9.


APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility.

Xia JF, Zhao XM, Song J, Huang DS.

BMC Bioinformatics. 2010 Apr 8;11:174. doi: 10.1186/1471-2105-11-174.


Learning a Markov Logic network for supervised gene regulatory network inference.

Brouard C, Vrain C, Dubois J, Castel D, Debily MA, d'Alché-Buc F.

BMC Bioinformatics. 2013 Sep 12;14:273. doi: 10.1186/1471-2105-14-273.


Supervised, semi-supervised and unsupervised inference of gene regulatory networks.

Maetschke SR, Madhamshettiwar PB, Davis MJ, Ragan MA.

Brief Bioinform. 2014 Mar;15(2):195-211. doi: 10.1093/bib/bbt034. Epub 2013 May 21.


Feature-based classification of native and non-native protein-protein interactions: Comparing supervised and semi-supervised learning approaches.

Zhao N, Pang B, Shyu CR, Korkin D.

Proteomics. 2011 Nov;11(22):4321-30. doi: 10.1002/pmic.201100217. Epub 2011 Oct 17.


Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data.

You ZH, Lei YK, Gui J, Huang DS, Zhou X.

Bioinformatics. 2010 Nov 1;26(21):2744-51. doi: 10.1093/bioinformatics/btq510. Epub 2010 Sep 3.


Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines.

González AJ, Liao L.

BMC Bioinformatics. 2010 Oct 29;11:537. doi: 10.1186/1471-2105-11-537.


IRIS: a method for reverse engineering of regulatory relations in gene networks.

Morganella S, Zoppoli P, Ceccarelli M.

BMC Bioinformatics. 2009 Dec 23;10:444. doi: 10.1186/1471-2105-10-444.


Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.

Su EC, Chang JM, Cheng CW, Sung TY, Hsu WL.

BMC Bioinformatics. 2012;13 Suppl 17:S13. doi: 10.1186/1471-2105-13-S17-S13. Epub 2012 Dec 13.


Growing functional modules from a seed protein via integration of protein interaction and gene expression data.

Maraziotis IA, Dimitrakopoulou K, Bezerianos A.

BMC Bioinformatics. 2007 Oct 23;8:408.


SSLPred: predicting synthetic sickness lethality.

Bandyopadhyay N, Ranka S, Kahveci T.

Pac Symp Biocomput. 2012:7-18.


Exploitation of genetic interaction network topology for the prediction of epistatic behavior.

Alanis-Lobato G, Cannistraci CV, Ravasi T.

Genomics. 2013 Oct;102(4):202-8. doi: 10.1016/j.ygeno.2013.07.010. Epub 2013 Jul 25.


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