Search Disease/ Diagnostics
contains
exact
begins with
ends with
use * as joker
show
10
50
100
results
Recommended Name:
contains
exact
begins with
ends with
use * as joker
EC Number:
contains
exact
begins with
ends with
use * as joker
PubMed ID:
contains
exact
begins with
ends with
use * as joker
Title of Publication:
contains
exact
begins with
ends with
use * as joker
Category:
contains
exact
begins with
ends with
use * as joker
Confidence Level:
=
<
>
between min-max
use * as joker
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
0.4925
0.7323
0.2677
0.66
1
126848
therapeutic application
3
0.9744
0.5672
0.7638
0.2362
0.717
0.9833
189085
therapeutic application
2
0.8596
0.7313
0.7953
0.2047
0.7903
0.8667
320561
therapeutic application
1
0.8636
0.8507
0.8504
0.1496
0.8571
0.85
478466
ongoing research
4
0.7931
0.219
0.5686
0.4314
0.3433
0.9394
144718
ongoing research
3
0.7455
0.3905
0.6176
0.3824
0.5125
0.8586
300064
ongoing research
2
0.7465
0.5048
0.6569
0.3431
0.6023
0.8182
388890
ongoing research
1
0.7386
0.619
0.6912
0.3088
0.6736
0.7677
470819
diagnostic usage
4
0.8966
0.3059
0.6457
0.3543
0.4561
0.9667
190192
diagnostic usage
3
0.8627
0.5176
0.7257
0.2743
0.6471
0.9222
315083
diagnostic usage
2
0.8226
0.6
0.7429
0.2571
0.6939
0.8778
388039
diagnostic usage
1
0.7531
0.7176
0.7486
0.2514
0.7349
0.7778
522837
causal interaction
4
0.8947
0.2125
0.4828
0.5172
0.3434
0.9562
281638
causal interaction
3
0.8767
0.2667
0.5093
0.4907
0.4089
0.9343
354446
causal interaction
2
0.8559
0.3958
0.5729
0.4271
0.5413
0.8832
465674
causal interaction
1
0.8333
0.5208
0.6286
0.3714
0.641
0.8175
597734
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