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Results: 1 to 20 of 41

1.

Role of the host genetic variability in the influenza A virus susceptibility.

Arcanjo AC, Mazzocco G, de Oliveira SF, Plewczynski D, Radomski JP.

Acta Biochim Pol. 2014;61(3):403-19. Epub 2014 Sep 4. Review.

2.

Ensemble learning prediction of protein-protein interactions using proteins functional annotations.

Saha I, Zubek J, Klingström T, Forsberg S, Wikander J, Kierczak M, Maulik U, Plewczynski D.

Mol Biosyst. 2014 Apr;10(4):820-30. doi: 10.1039/c3mb70486f. Epub 2014 Jan 27.

PMID:
24469380
3.

PPIcons: identification of protein-protein interaction sites in selected organisms.

Sriwastava BK, Basu S, Maulik U, Plewczynski D.

J Mol Model. 2013 Sep;19(9):4059-70. doi: 10.1007/s00894-013-1886-9. Epub 2013 Jun 2.

4.

Consensus classification of human leukocyte antigen class II proteins.

Saha I, Mazzocco G, Plewczynski D.

Immunogenetics. 2013 Feb;65(2):97-105. doi: 10.1007/s00251-012-0665-6. Epub 2012 Nov 16. Erratum in: Immunogenetics. 2013 Apr;65(4):313.

5.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

Plewczynski D, Basu S, Saha I.

Amino Acids. 2012 Aug;43(2):573-82. doi: 10.1007/s00726-012-1290-2. Epub 2012 May 4.

6.

Improved differential evolution for microarray analysis.

Saha I, Plewczynski D, Maulik U, Bandyopadhyay S.

Int J Data Min Bioinform. 2012;6(1):86-103.

PMID:
22479820
7.

Fuzzy clustering of physicochemical and biochemical properties of amino acids.

Saha I, Maulik U, Bandyopadhyay S, Plewczynski D.

Amino Acids. 2012 Aug;43(2):583-94. doi: 10.1007/s00726-011-1106-9. Epub 2011 Oct 13.

8.

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines.

Chatterjee P, Basu S, Kundu M, Nasipuri M, Plewczynski D.

J Mol Model. 2011 Sep;17(9):2191-201. doi: 10.1007/s00894-011-1102-8. Epub 2011 May 19.

9.

PPI_SVM: prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables.

Chatterjee P, Basu S, Kundu M, Nasipuri M, Plewczynski D.

Cell Mol Biol Lett. 2011 Jun;16(2):264-78. doi: 10.2478/s11658-011-0008-x. Epub 2011 Mar 20.

PMID:
21442443
10.

GIDMP: good protein-protein interaction data metamining practice.

Plewczynski D, Klingström T.

Cell Mol Biol Lett. 2011 Jun;16(2):258-63. doi: 10.2478/s11658-011-0004-1. Epub 2011 Mar 9.

PMID:
21394448
11.

Detailed mechanism of squalene epoxidase inhibition by terbinafine.

Nowosielski M, Hoffmann M, Wyrwicz LS, Stepniak P, Plewczynski DM, Lazniewski M, Ginalski K, Rychlewski L.

J Chem Inf Model. 2011 Feb 28;51(2):455-62. doi: 10.1021/ci100403b. Epub 2011 Jan 13.

PMID:
21229992
12.

HarmonyDOCK: the structural analysis of poses in protein-ligand docking.

Plewczynski D, Philips A, Von Grotthuss M, Rychlewski L, Ginalski K.

J Comput Biol. 2014 Mar;21(3):247-56. doi: 10.1089/cmb.2009.0111. Epub 2010 Nov 20.

PMID:
21091053
13.

Brainstorming: weighted voting prediction of inhibitors for protein targets.

Plewczynski D.

J Mol Model. 2011 Sep;17(9):2133-41. doi: 10.1007/s00894-010-0854-x. Epub 2010 Sep 21.

14.

Protein-protein interaction and pathway databases, a graphical review.

Klingström T, Plewczynski D.

Brief Bioinform. 2011 Nov;12(6):702-13. doi: 10.1093/bib/bbq064. Epub 2010 Sep 17.

15.

VoteDock: consensus docking method for prediction of protein-ligand interactions.

Plewczynski D, Łaźniewski M, von Grotthuss M, Rychlewski L, Ginalski K.

J Comput Chem. 2011 Mar;32(4):568-81. doi: 10.1002/jcc.21642. Epub 2010 Sep 1.

PMID:
20812324
16.

Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database.

Plewczynski D, Łaźniewski M, Augustyniak R, Ginalski K.

J Comput Chem. 2011 Mar;32(4):742-55. doi: 10.1002/jcc.21643. Epub 2010 Sep 1.

PMID:
20812323
17.

Species used for drug testing reveal different inhibition susceptibility for 17beta-hydroxysteroid dehydrogenase type 1.

Möller G, Husen B, Kowalik D, Hirvelä L, Plewczynski D, Rychlewski L, Messinger J, Thole H, Adamski J.

PLoS One. 2010 Jun 8;5(6):e10969. doi: 10.1371/journal.pone.0010969.

18.

AMS 3.0: prediction of post-translational modifications.

Basu S, Plewczynski D.

BMC Bioinformatics. 2010 Apr 28;11:210. doi: 10.1186/1471-2105-11-210.

19.

Virtual high throughput screening using combined random forest and flexible docking.

Plewczynski D, von Grotthuss M, Rychlewski L, Ginalski K.

Comb Chem High Throughput Screen. 2009 Jun;12(5):484-9.

PMID:
19519327
20.

Performance of machine learning methods for ligand-based virtual screening.

Plewczynski D, Spieser SA, Koch U.

Comb Chem High Throughput Screen. 2009 May;12(4):358-68. Review.

PMID:
19442065
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