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 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
0.4706
0.7188
0.2813
0.64
1
223788
therapeutic application
3
0.95
0.5588
0.75
0.25
0.7037
0.9667
314621
therapeutic application
2
0.8689
0.7794
0.8203
0.1797
0.8217
0.8667
514678
therapeutic application
1
0.8451
0.8824
0.8516
0.1484
0.8633
0.8167
875560
ongoing research
4
0.7353
0.2381
0.5637
0.4363
0.3597
0.9091
261471
ongoing research
3
0.7213
0.419
0.6176
0.3824
0.5301
0.8283
463037
ongoing research
2
0.7303
0.619
0.6863
0.3137
0.6701
0.7576
641621
ongoing research
1
0.6931
0.6667
0.6765
0.3235
0.6796
0.6869
794689
diagnostic usage
4
0.8438
0.3293
0.6471
0.3529
0.4737
0.9432
222460
diagnostic usage
3
0.8148
0.5366
0.7176
0.2824
0.6471
0.8864
469756
diagnostic usage
2
0.7969
0.622
0.7412
0.2588
0.6986
0.8523
598723
diagnostic usage
1
0.75
0.6951
0.7412
0.2588
0.7215
0.7841
803456
causal interaction
4
1
0.0172
0.3743
0.6257
0.0338
1
60629
causal interaction
3
0.8452
0.3047
0.5219
0.4781
0.4479
0.9023
660923
causal interaction
2
0.8609
0.4249
0.5902
0.4098
0.569
0.8797
839252
causal interaction
1
0.8412
0.6137
0.6803
0.3197
0.7097
0.797
1084408

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