Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators

BMC Research Notes

Volumn 7, Issue 1, 2014, Pages

  Muthukrishnan, Selvaraj ^a,b,d   Puri, Munish ^a,c,d   Lefevre, Christophe ^c  

^a PUNJABI UNIVERSITY   (India)

^b INSTITUTE OF MICROBIAL TECHNOLOGY   (India)

^c DEAKIN UNIVERSITY   (Australia)

^d 2CSIR IMTECH ^*  (India)

Author keywords

Comparative analysis; Pg activators; Plasminogen activators; SAK; SK; Staphylokinase; Streptokinase; Support vector machine; SVM; Tissue plasminogen activators; tPA; UK; Urokinase

Indexed keywords

AMINO ACID; DIPEPTIDE; PLASMINOGEN ACTIVATOR;

AMINO ACID SEQUENCE; ANIMAL; ARTICLE; CHEMICAL MODEL; CHEMISTRY; CLASSIFICATION; ENZYMOLOGY; EUKARYOTIC CELL; HUMAN; INTERNET; METHODOLOGY; MOLECULAR GENETICS; PROKARYOTIC CELL; PROTEIN DATABASE; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

AMINO ACID SEQUENCE; AMINO ACIDS; ANIMALS; DATABASES, PROTEIN; DIPEPTIDES; EUKARYOTIC CELLS; HUMANS; INTERNET; MODELS, CHEMICAL; MOLECULAR SEQUENCE DATA; PLASMINOGEN ACTIVATORS; PROKARYOTIC CELLS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SEQUENCE ANALYSIS, PROTEIN; SUPPORT VECTOR MACHINES;

EID: 84894235660     PISSN: None     EISSN: 17560500     Source Type: Journal    
DOI: 10.1186/1756-0500-7-63     Document Type: Article

References (30)

1. Structure and function of the plasminogen/plasmin system. Castellino FJ, Ploplis VA, Thromb Haemost 2005 93 4 647 654 15841308 (Pubitemid 40485399)
2. Interaction of staphylokinase with different molecular forms of plasminogen. Lijnen HR, Van Hoef B, Collen D, Eur J Biochem 1993 211 1-2 91 97 8425556 (Pubitemid 23039500)
3. Characterization of the murine plasminogen/urokinase-type plasminogen-activator system. Lijnen HR, Van Hoef B, Collen D, Eur J Biochem 1996 241 3 840 848 10.1111/j.1432-1033.1996.00840.x 8944773 (Pubitemid 26379387)
4. Streptokinase-a clinically useful thrombolytic agent. Banerjee A, Chisti Y, Banerjee UC, Biotechnol Adv 2004 22 4 287 307 10.1016/j.biotechadv.2003.09. 004 14697452 (Pubitemid 38024782)
5. Intra arterial fibrinolytic therapy, efficacy of streptokinase vsurokinase. Belkin M, Belkin B, Bucknam CA, Straub JJ, Lowe R, Arch Surg 1986 121 7 769 773 10.1001/archsurg.1986.01400070035007 3718210 (Pubitemid 16075799)
6. Comparison of streptokinase, urokinase and recombinant tissue plasminogen activator in an in vitro model of venous thrombosis. Ouriel K, Welch EL, Shortell CK, Geary K, Fiore WM, Cimino C, J Vasc Surg 1995 22 5 593 597 10.1016/S0741-5214(95)70045-5 7494361
7. Plasminogen activators: a comparison. Baruah DB, Dash RN, Chaudhari MR, Kadam SS, Vascul Pharmacol 2006 44 1 1 9 10.1016/j.vph.2005.09.003 16275118 (Pubitemid 41799584)
8. Role of the N-terminal region of staphylokinase (SAK): evidence for the participation of the N-terminal region of SAK in the enzyme-substrate complex formation. Rajamohan G, Dikshit KL, FEBS Lett 2000 474 2-3 151 158 10838076 (Pubitemid 30330825)
9. Function of the 90-loop (Thr90-Glu100) region of staphylokinase in plasminogen activation probed through site-directed mutagenesis and loop deletion. Rajamohan G, Dahiya M, Mande SC, Dikshit KL, Biochem J 2002 365 379 389 10.1042/BJ20011647 11936951 (Pubitemid 36135310)
10. Comparative statistics for DNA and protein sequences: multiple sequence analysis. Karlin S, Ghandour G, Proc Natl Acad Sci U S A 1985 82 18 6186 6190 10.1073/pnas.82.18.6186 3929250 (Pubitemid 16223923)
11. Methods and algorithms for statistical analysis of protein sequences. Brendel V, Bucher P, Nourbakhsh IR, Blaisdell BE, Karlin S, Proc Natl Acad Sci U S A 1992 15;89 6 2002 2006
12. Comparative analysis of complete genome sequences of three avian coronaviruses reveals a novel group 3c coronavirus. Woo PC, Lau SK, Lam CS, Lai KK, Huang Y, Lee P, Luk GS, Dyrting KC, Chan KH, Yuen KY, J Virol 2009 83 2 908 917 10.1128/JVI.01977-08 18971277
13. MHCBN 4.0: a database of MHC/TAP binding peptides and T-cell epitopes. Lata S, Bhasin M, Raghava GPS, BMC Res Notes 2009 2 61 10.1186/1756-0500-2-61 19379493
14. Expression and characterization of a novel plasminogen activator from Agkistrodon halys venom. Park D, Kim H, Chung K, Kim DS, Yun Y, Toxicon 1998 36 12 1807 1819 10.1016/S0041-0101(98)00090-7 9839665 (Pubitemid 28532759)
15. Vampire bat salivary plasminogen activator (desmoteplase) a unique fibrinolytic enzyme that does not promote neurodegeneration. Liberatore GT, Samson A, Bladin C, Schleuning WD, Medcalf RL, Stroke 2003 34 2 537 543 10.1161/01.STR.0000049764.49162.76 12574572 (Pubitemid 36206361)
16. Oxypred: prediction and classification of oxygen-binding proteins. Muthukrishnan S, Garg A, Raghava GP, Genomics Proteomics Bioinformatics 2007 5 3-4 250 252 18267306 (Pubitemid 351215124)
17. Making large scale SVM learning practical. Joachims T, Advances in Kernel Methods-Support Vector Learning Cambridge: MIT Press, Scholkopf B, Burges C, Smola A, 1999 169 184
18. Vapnik V, The Nature of Statistical Learning Theory NewYork: Springer 1995
19. Sequence similarity analysis of Escherichia coli proteins: functional and evolutionary implications. Koonin EV, Tatusov RL, Rudd KE, Proc Natl Acad Sci U S A 1995 92 25 11921 11925 10.1073/pnas.92.25.11921 8524875 (Pubitemid 26014222)
20. A fast word search algorithm for the representation of sequence similarity in genomic DNA. Lefèvre C, Ikeda JE, Nucleic Acids Res 1994 22 3 404 411 10.1093/nar/22.3.404 8127677 (Pubitemid 24160862)
21. Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. Garg A, Bhasin M, Raghava GP, J Biol Chem 2005 280 15 14427 14432 10.1074/jbc.M411789200 15647269 (Pubitemid 40562782)
22. Prediction of protein subcellular locations using fuzzy k-NN method. Huang Y, Li Y, Bioinformatics 2004 20 1 21 28 10.1093/bioinformatics/btg366 14693804 (Pubitemid 38088336)
23. An introduction to ROC analysis. Fawcett T, Pattern Recogn Lett 2006 27 861 874 10.1016/j.patrec.2005.10.010
24. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Bradley AP, Pattern Recogn 1997 30 7 1145 1159 10.1016/S0031-3203(96)00142-2 (Pubitemid 127406521)
25. A simple generalization of the area under the ROC curve to multiple class classification problems. Hand DJ, Till RJ, Mach Learn 2001 45 2 171 186 10.1023/A:1010920819831 (Pubitemid 33635984)
26. Prediction of RNA binding sites in a protein using SVM and PSSM profile. Kumar M, Gromiha MM, Raghava GPS, Proteins 2007 71 189 194
27. Support vector machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs. Rashid M, Saha S, Raghava GPS, BMC Bioinformatics 2007 8 337 10.1186/1471-2105-8-337 17854501 (Pubitemid 350278741)
28. Prediction of mitochondrial proteins using support vector machine and hidden markov model. Kumar M, Verma R, Raghava GPS, J Biol Chem 2006 281 5357 5363 16339140 (Pubitemid 43847629)
29. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Li W, Godzik A, Bioinformatics 2006 22 13 1658 1659 10.1093/bioinformatics/btl158 16731699 (Pubitemid 43985301)
30. Analysis and prediction of cancerlectins using evolutionary and domain information. Kumar R, Panwar B, Chauhan JS, Raghava GP, BMC Res Notes 2011 20;4 237