With this information how likely/often will a motif appear in the dataset.


ONDEXView hss = graph.getRelationsOfRelationType(graph.getMetaData().getRelationType("h_s_s"));
int j = 0;
for (ONDEXRelation r : hss) {
ONDEXConcept to = r.getToConcept();
ONDEXConcept from = r.getFromConcept();
if (to.getOfType().getId().equals("Protein") && from.getOfType().getId().equals("Protein")) {
j++;
}
}
System.out.println("hss " + j);


Ropinirole

There were 16 instances of motifs(2) involving Ropinirole.

Useful IDs:

• Chlorpromazine 120943
• Trimeprazine 120836
  
• Ropinrole 118054
• Pramipexole 118131
• Bupropion 118567
• Sibutramine 118536
• Lidocaine 120780
• Celecoxib 121260
• Finasteride 118603
• Raloxifene 118164
• Topiramate 116780

Example output from Java (cutoff dataset):

Code was ran on the updated dataset:

• There were 70,866 instances of this motif definition in the dataset.
• However, this doesn’t address the issue of the OMIM database. So many of these will not have true “Diseases”. This would probably be best addressed when trying to score this type, if its even possible to do this.

It would be a good idea to cross check this with Chlorpromazine, but I need to get Linux up and running properly in a dual-boot due to memory issues in Windows (also not being able to print concept names is a problem).

Wrote a toString() method for a nicer output for testing purposes:

Drug1 <--> Drug2 --> Target

I wanted the Concepts name for example Chlorpromazine:

Chlorpromazine <--> Trimeprazine --> Histamine H1 Receptor

However , the ondex API is not working for printing names? – so using getId()’s for now.

Output

Here is some current output of the basic semantic motif definition.

Differences

This new implementation is different to the previously developed code by Cockell, S.J. for the 2010 paper. However, in the cutoff_data.xml.gz 157 motifs were identified, the same as the using the other method. However, there is a difference when using the code on the larger dataset ib2010_data.xml.gz. Previously there were 26,693 motifs identified compared with 29,633 using the new implementation, a difference of 2,940.

Note

It should also be reminded that it was discovered in the proper dataset:

• Drugs seem to have the ConceptClass “Comp:Compound“.
• Rather than the ConceptClass “Compound“.
• There are 70 of these, none of which seem to bind to a target.
• There are over 8k concepts of the former, so I will use this.

Example – TanimotoCoefficient of a “sim” relation:


int i = 0;
for (ONDEXRelation r : relationview) {
// Get an attribute test;
AttributeName an = graph.getMetaData().getAttributeName("TanimotoCoefficient");
GDS gds = r.getGDS(an);
System.out.println(i + "-" + gds.getValue());
i++;
}


General terminal output seems to be done like this in other classes :-
fireEventOccurred(new GeneralOutputEvent("Unknown Relation type set :" + rts + " ignoring", "[Filter - start]"));

Thoughts :-

Store the semantic motifs as ONDEXGraph’s in a Set.

Create these subgraphs using the drug1, drug2, drug1Relations and drug2Relations, used in the code to count motifs. Does ONDEXViewFunctions provide this? (.and, .andNot and .or) Do these automatically give the concepts when “and’ing” to ONDEXView’s of relations.

- But only if it is a ‘match‘.

Testing gave :-

• 157 semantic motifs for the cutdown_set.xml.gz dataset.
• 26,693 sematnic motifs for the ib2010_data.xml.gz dataset.

Which is the same as what the paper stated, showing the newest code is working correctly.

Exception in thread "main" java.lang.NoSuchMethodError: net.sourceforget.ondex.core.ONDEXView.iterator()Ljava/util/Iterator;

Tried a number of methods but still getting the Iterator error no matter what.

Trying a Set/HashSet approach.

• Compound (Chemical Compound (used as drug))
• Disease (Human Diseases)
• EC
• Protein
• Publication
• Target (Target of Compound concept)

RelationTypes

• bi_to (binds to)
• cat_c (catalysing class)
• inv_in (involved in)
• h_s_s
• is_a
• it_wi (interacts with)
• pub_in (published in)
• r (is related to)
• sim

Data Sources

• UC (Unclassified)
• UC:UNIPROTKB (Collapsed CV Set)
• UNIPROTKB
  

Evidence Types

• ACC (Accession based mapping)
• BLAST (BLAST based mapping)
• IMPD (Imported from database)
• IMPD_MANUALLY_CURATED