Hi Khader,

Below are the long answers for your questions. Hope they are useful.

1. HPO has three namespaces (sub-ontologies). This situation is very similar to GO and its sub-ontologies (Biological Process, Molecular Function, Cellular Component). For this reason, you have to calculate semantic similarity for each sub ontology, and then take their sum as your final semantic similarity. Alternatively, for HPO, usually the sub-ontology (Phenotypic Abnormality) is useful, and other two sub-ontologies (Mode of Inheritance; ONset and clinical course) are not well-defined.

2. First, make it clear that semantic similarity is a type of comparison to assess the degree of relatedness between two entities. It can be between two terms, but also can be between two genes annotated by terms. To do these, information content (IC) of a term is defined as the negative 10-based log-transformed frequency of genes annotated to that term. This definition considers the actual usage of a term (the frequency of annotated genes it has) to measure how specific and informative the term is. The function http://supfam.org/dnet/dDAGtermSim.html is to calculate semantic similarity between terms, which is then used by the function http://supfam.org/dnet/dDAGgeneSim.html to calculate semantic similarity between genes. When we are talking about semantic similarity between terms, the semantic similairty is NOT about their distance in the ontology hierarchy (actually organised as a DAG: directed acyclic graph without cycles). Depending on which methods to use, the meaning of semantic similarity can be different. If you choose the method 'Resnik', then semantic similarity is the information content (IC) at most informative common ancestor (MICA) of two terms (of your interest). MICA for HP:0000062 x HP:0000062 is HP:0000062 (who's IC is 1.958076). MICA for HP:0000062 x HP:0010931 is the root of ontology. Always, IC at the root is zero.

3. As for the Disease Ontology (DO), there is no sub-ontology. So the situation is very simple. Here is the code how to do it using dnet.

3a) if you are interested in the semantic similarity between DO terms.

# 1) load DO as igraph object (note: it is NOT part of the package built-in data, so you have to load via the function dRDataLoader)
ig.DO <- dRDataLoader(RData = "ig.DO")
g <- ig.DO

# 2) load human genes annotated by DO (note: it is NOT part of the package built-in data, so you have to load via the function dRDataLoader)
org.Hs.egDO <- dRDataLoader(RData = "org.Hs.egDO")

# 3) prepare for ontology and its annotation information
dag <- dDAGannotate(g, annotations="org.Hs.egDO", path.mode="all_paths", verbose=TRUE)

# 4) calculate pair-wise semantic similarity between 5 randomly chosen terms
terms <- sample(V(dag)$name, 5)
sim <- dDAGtermSim(g=dag, terms=terms, method="Schlicker", parallel=FALSE)
sim

3b) if you are interested in the semantic similarity between human genes (annotatable by DO terms).

# 1) load DO as igraph object (note: it is NOT part of the package built-in data, so you have to load via the function dRDataLoader)
ig.DO <- dRDataLoader(RData = "ig.DO")
g <- ig.DO

# 2) load human genes annotated by DO (note: it is NOT part of the package built-in data, so you have to load via the function dRDataLoader)
org.Hs.egDO <- dRDataLoader(RData = "org.Hs.egDO")

# 3) prepare for ontology and its annotation information
dag <- dDAGannotate(g, annotations="org.Hs.egDO", path.mode="all_paths", verbose=TRUE)

# 4) calculate pair-wise semantic similarity between 5 randomly chosen genes
allgenes <- unique(unlist(V(dag)$annotations))
genes <- sample(allgenes,5)
sim <- dDAGgeneSim(g=dag, genes=genes, method.gene="BM.average", method.term="Resnik", parallel=FALSE, verbose=TRUE)
sim