Bio-medical Ontologies Maintenance and Change Management

Extraction of Constraints from Biological Data 183
2. In literature evidences can be found that the crystal structure of MTH1020
protein reveals an Ntn-hydrolase fold [22].
3. The results can be compared to the SCOP constraints (which are known),
and confirm the correctness of this relationship (i.e., it is not an error).
Finally, applying the same procedure also to the CATH database, we discovered
the anomalies reported in Fig. 3. Among these, we noticed the one for the
hypothetical protein MTH1020. Thus, the method applied independently on two
different databases reveals the same anomaly. This result confirms the consistency
of the proposed method for anomaly discovery.
5.4 Quasi Functional Dependencies
For the purpose of identifying functional dependencies, the dependency degree between
couples of attributes must be equal to 1 (see Eq. (3)). We verified that the
proposed method is sound and complete by means of two experiments, which
show that it correctly identifies all and only the functional dependencies contained
in the database.
In the first experiment we executed the algorithm on the SCOP database with
the lowest support value. Table 4 shows the detected functional dependencies.
The first two columns contain the attributes which are functionally dependent
(their names are explained in the following example). The third column represents
the dependency degree obtained by applying Eq. (3) on the original data.
Only the couples of attributes with dependency degree of 1 are shown. Since the
SCOP database constraints are known, we exploited its structural knowledge to
prove the correctness of our method. Our method detects all and only the functional
dependencies in the SCOP database. Hence, it is sound (i.e., it only detects
correct functional dependencies) and complete (i.e., all known functional dependencies
are detected).
In the second experiment we performed a simulation of fault injection tests. In
this way, we demonstrate that also in presence of errors, our method is sound and
complete, because it still detects all and only the non-faulted dependencies. We
changed random values at different levels of the hierarchy, by substituting the actual
value with one randomly taken from another branch of the tree. This kind of
misclassification is rather subtle to identify, since the value is acceptable (i.e., it is
valid and it is not misspelled), but it assigns a particular protein to the wrong class
for one or more levels of the hierarchy. The fourth column of Table 4 represents
the dependency degree after the fault injection simulation, where a random fault
has been injected for some levels of the classification hierarchy. All the affected
attributes, represented in bold in the first two columns, report dependencies whose
value falls below 1. Thus, a quasi functional dependency analysis can be performed
to detect all the injected faults.