Any feedback?
Please rate this page
(search_result.php)
(0/150)

BRENDA support

Search Disease/ Diagnostics

show results
Refine your search

 Diseases and/or Diagnostics which are related to enzyme classes

 Please choose one of the four different Confidence Levels:
 Confidence Level 1: Precision > 75%, Accuracy > 70%
 Confidence Level 2: Precision > 77%, Accuracy > 70%
 Confidence Level 3: Precision > 85%, Accuracy > 80%
 Confidence Level 4: Precision > 95%, Accuracy > 80%

DRENDA (Disease Related ENzyme information DAtabase) [1]
DRENDA is a new supplement to BRENDA providing disease-related enzyme information on the absence or malfunction of enzymes which have a major influence on the metabolism, regulation, and immunity etc. causing severe diseases. The development of DRENDA focuses on the automatic search of enzyme-disease relations from titles and abstracts of the PubMed database [2] and its classification. This approach is based on a text-mining method, supported by:
  • BRENDA vocabularies (~100 000 items)
  • EC numbers
  • Enzyme names (including synonyms)
  • MeSH terms for diseases and metabolic diorders from the NCBI database (~23 500 terms)
This approach resulted in 0.9 million enzyme-disease combinations extracted from the literature. Further on the enzyme-disease relations are classified into four categories using machine learning methods via Support Vector Machines [3]:
  • causal interaction: if the absence or the malfuction of an enzyme causes a disease
  • therapeutic application: the therapeutic usage of an enzyme as drug target or therapeutic agent is described
  • diagnostic usage: the enzyme is used for a diagnostic approach/analysis tests or the malfunction of an enzyme is detected to diagnose a disease
  • ongoing research: the research about the enzyme-disease relation is still in progress
Enzyme-disease relationships and their classification in BRENDA [1]

Category
Confidence Level
Precision
Recall
Accuracy
Error
F1 Score
Specificity
Entries
therapeutic application
4
1
0.4559
0.7109
0.2891
0.6263
1
242401
therapeutic application
3
0.95
0.5588
0.75
0.25
0.7037
0.9667
340595
therapeutic application
2
0.8909
0.7206
0.8047
0.1953
0.7967
0.9
467607
therapeutic application
1
0.8267
0.9118
0.8516
0.1484
0.8671
0.7833
866975
ongoing research
4
0.7419
0.219
0.5588
0.4412
0.3382
0.9192
238692
ongoing research
3
0.75
0.3714
0.6127
0.3873
0.4968
0.8687
446755
ongoing research
2
0.7195
0.5619
0.6618
0.3382
0.631
0.7677
655831
ongoing research
1
0.7021
0.6286
0.6716
0.3284
0.6633
0.7172
797457
diagnostic usage
4
0.8667
0.3133
0.6453
0.3547
0.4602
0.9551
223334
diagnostic usage
3
0.8235
0.506
0.7093
0.2907
0.6269
0.8989
499479
diagnostic usage
2
0.8033
0.5904
0.7326
0.2674
0.6806
0.8652
659656
diagnostic usage
1
0.7531
0.7349
0.7558
0.2442
0.7439
0.7753
842322
causal interaction
4
0.8636
0.161
0.4472
0.5528
0.2714
0.9549
393261
causal interaction
3
0.8506
0.3136
0.5257
0.4743
0.4582
0.9023
709408
causal interaction
2
0.8364
0.3898
0.561
0.439
0.5318
0.8647
833760
causal interaction
1
0.8446
0.5297
0.6369
0.3631
0.651
0.8271
1031110

Reference:
  • [1] Söhngen,C., Chang,A., Schomburg,D. (2011) Development of a classification scheme for disease-related enzyme information. BMC Bioinformatics, 12, 329.
  • [2] Sayers,E.W., Barrett,T., Benson,D.A., Bolton,E., Bryant,S.H., Canese,K., Chetvernin,V., Church,D.M., Dicuccio,M., Federhen,S., Feolo,M., Fingerman,I.M., Geer, LY, Helmberg,W., Kapustin,Y., Krasnov,S., Landsman,D., Lipman,D.J., Lu,Z., Madden,T.L., Madej,T., Maglott,R., Marchler-Bauer,A., Miller,V., Karsch-Mizrachi,I., Ostell,J., Panchenko,A., Phan,L., Pruitt,K.D., Schuler,G.D., Sequeira,E., Sherry,S.T., Shumway,M., Sirotkin,K., Slotta,D., Souvorov,A., Starchenko,G., Tatusova,T.A., Wagner,L., Wang,Y., Wilbur,W.J., Yaschenko,E., Ye,J. (2012) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res., 40, D13-D25.
  • [3] Joachims T. In: Advances in Kernel Methods - Support Vector Learning. Schölkopf B, Burges C, Smola A, editor. Cambridge, MA: MIT Press; (1999). Making large-Scale SVM Learning Practical; pp. 169-184.
Funding:
This work was supported by SLING: Serving Life-science Information for the Next Generation