V1.1
 
           
 


Go to the Gene Ontology(GO) website

 
Semantic Similairity of:
This tool is used to measure the semantic similarity of pairwise terms sets input through text files This tool is used to measure the semantic similarity of two groups of terms input through text files This tool is used to measure the semantic similarity of pairwise terms sets input through text files
 
This tool is used to measure the semantic similarity of two groups of terms input through text files
1.Enter two groups of GO terms in the format of one term ID excluding "GO:" per line in the input fields.
2.Select the measurements for semantic similarity computing, only surpports SSDD, G-SESAME and simUI now.
3.Press the "Submit" button and wait for the result page.
 
First term list
Second term list
 
 
 
Measurement
 
 

 
 
This tool is used to measure the semantic similarity of two GO Terms.
1.Enter GO term accession numbers in input fields excluding "GO:", such as 0005739.
2.Select the measurements for semantic similarity computing, only surpports SSDD, G-SESAME and simUI now.
3.Press the "Submit" button and wait for the result.
 
GO Term1
 
GO Term2
 
Measurement
 
 
Result
 
 
 
 
 
This tool is used to measure the semantic similarity of pairwise terms sets input through text files
1.Enter pairwise GO terms ID excluding "GO: "in the format of one pair splited by a 'Space' or 'Tab' per line.
2.Select the measurements for semantic similarity computing, only surpports SSDD, G-SESAME and simUI now.
3.Press the "Submit" button and wait for the result page.
 
Pairwise Terms Set
 
   
 
 
Measurement
   
 
 
 
            
GO Term Analysis Tool

Want can we use this tool to do?

     The 'GO Term Analysis Tool' can be used to compute the semantic similarity of GO terms in the forms of "two terms", "two term groups", and "pairwise term groups". The algorithms ultilized by this tool are SSDD, G-SESAME and simUI, coupled with Best match average (BMA) approach now. Soon, more and more algorithms will be integrated into.

      Furthermore, you can easylly view the overlapped DAG of any two GO term.(new)


What is SSDD?

     The Shortest Semantic Differentiation Distance (SSDD) algorithm measures semantic similarity between GO terms from a novel perspective. In SSDD, a pair of terms is represented as overlapping directed acyclic graphs, which is then viewed as a semantic genealogy. The semantic heredity from a parent to its children is regarded as a process of semantic differentiation. Then semantic distance between two terms is calculated by the capacity of redifferentiation from one term to the other. In comprehensive evaluations either against human rating or using a benchmark dataset, SSDD compares favorably with other methods and performs slightly better than simUI, another intrinsic method. SSDD addresses the issues of shallow and identical annotation and can furthermore distinguish sibling semantic similarity, in addition to its intrinsic to GO. It provides an alternative to both methods that use external resources and methods “intrinsic” to GO with comparable performance.


Where is SSDD better than other methods?

       1. SSDD is a completely novel insight into GO semantic similarity.Borrowing from the biological process of cellular differentiation, the semantic of each GO term is represented as semantic totipotency, and so the semantic heredity from a parent to its children is regarded as a process of semantic differentiation.

       2. SSDD jumps out of the reliance on external sources of data, e.g. the GOA datasets, thus to be intrinsic to the ontology;

       3. SSDD, most prominently, can overcome the issues of shallow annotations and identical annotations, and furthermore distinguish the similarity of sibling terms.

 
If you have any problem of running GFSAT, please contact Prof. Guo Maozu, Xu Yungang