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Items: 18

1.

The best models of metabolism.

Voit EO.

Wiley Interdiscip Rev Syst Biol Med. 2017 Nov;9(6). doi: 10.1002/wsbm.1391. Epub 2017 May 19. Review.

PMID:
28544810
2.

Implementation and comparison of kernel-based learning methods to predict metabolic networks.

Roche-Lima A.

Netw Model Anal Health Inform Bioinform. 2016;5:26. Epub 2016 Jul 15.

3.

The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems.

Reafee W, Salim N, Khan A.

PLoS One. 2016 May 6;11(5):e0154848. doi: 10.1371/journal.pone.0154848. eCollection 2016.

4.

Systematically Prioritizing Functional Differentially Methylated Regions (fDMRs) by Integrating Multi-omics Data in Colorectal Cancer.

Fan H, Zhao H, Pang L, Liu L, Zhang G, Yu F, Liu T, Xu C, Xiao Y, Li X.

Sci Rep. 2015 Aug 4;5:12789. doi: 10.1038/srep12789.

5.

Classifying pairs with trees for supervised biological network inference.

Schrynemackers M, Wehenkel L, Babu MM, Geurts P.

Mol Biosyst. 2015 Aug;11(8):2116-25. doi: 10.1039/c5mb00174a.

6.

Metabolic network prediction through pairwise rational kernels.

Roche-Lima A, Domaratzki M, Fristensky B.

BMC Bioinformatics. 2014 Sep 26;15:318. doi: 10.1186/1471-2105-15-318.

7.

On protocols and measures for the validation of supervised methods for the inference of biological networks.

Schrynemackers M, Küffner R, Geurts P.

Front Genet. 2013 Dec 3;4:262. doi: 10.3389/fgene.2013.00262. Review.

8.

A negative selection heuristic to predict new transcriptional targets.

Cerulo L, Paduano V, Zoppoli P, Ceccarelli M.

BMC Bioinformatics. 2013;14 Suppl 1:S3. doi: 10.1186/1471-2105-14-S1-S3. Epub 2013 Jan 14.

9.

An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds.

Nakamura M, Hachiya T, Saito Y, Sato K, Sakakibara Y.

BMC Bioinformatics. 2012;13 Suppl 17:S8. doi: 10.1186/1471-2105-13-S17-S8. Epub 2012 Dec 13.

10.

GENIES: gene network inference engine based on supervised analysis.

Kotera M, Yamanishi Y, Moriya Y, Kanehisa M, Goto S.

Nucleic Acids Res. 2012 Jul;40(Web Server issue):W162-7. doi: 10.1093/nar/gks459. Epub 2012 May 18.

11.

Discovering novel subsystems using comparative genomics.

Ferrer L, Shearer AG, Karp PD.

Bioinformatics. 2011 Sep 15;27(18):2478-85. doi: 10.1093/bioinformatics/btr428. Epub 2011 Jul 19.

12.

Non-homologous isofunctional enzymes: a systematic analysis of alternative solutions in enzyme evolution.

Omelchenko MV, Galperin MY, Wolf YI, Koonin EV.

Biol Direct. 2010 Apr 30;5:31. doi: 10.1186/1745-6150-5-31.

13.

Machine learning methods for metabolic pathway prediction.

Dale JM, Popescu L, Karp PD.

BMC Bioinformatics. 2010 Jan 8;11:15. doi: 10.1186/1471-2105-11-15.

14.

Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening.

Zhou X, Wong ST.

Proc IEEE Inst Electr Electron Eng. 2008 Aug 1;96(8):1310-1331.

15.

Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactions.

Yip KY, Gerstein M.

Bioinformatics. 2009 Jan 15;25(2):243-50. doi: 10.1093/bioinformatics/btn602. Epub 2008 Nov 17.

16.

Inferring biological networks with output kernel trees.

Geurts P, Touleimat N, Dutreix M, d'Alché-Buc F.

BMC Bioinformatics. 2007 May 3;8 Suppl 2:S4.

17.

A network perspective on the topological importance of enzymes and their phylogenetic conservation.

Liu WC, Lin WH, Davis AJ, Jordán F, Yang HT, Hwang MJ.

BMC Bioinformatics. 2007 Apr 11;8:121.

18.

Identifying metabolic enzymes with multiple types of association evidence.

Kharchenko P, Chen L, Freund Y, Vitkup D, Church GM.

BMC Bioinformatics. 2006 Mar 29;7:177.

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