Soybean Functional Network

        A database of Soybean Functional Gene Network (SoyFGN) and miRNA Functional Network (SoymiRFN)

                         Functional Gene Network (FGN) | miRNA Network (miRFN)
                                          miRNA-Target datasets | 3'-UTR | Gene Annotations| KEGG Pathway
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About Gene Functional Similarity

Gene Functional Similarity

1. Gene Ontology (GO)

Gene Funtional SimilarityThe 'Gene Functional Simialrtiy Analysis Tool¬†‚ÄĒGFSAT'¬†is a web tool that can be used to Analysis the functional similarity of genes in the forms of "two genes", "two groups genes ", and "pairwise genes group" based on GO Annotation(GOA) and GO semantic similarity measurements. The semantic similarity measurements ultilized by GFSAT 1.1 are SSDD, G-SESAME and simUI now. Soon, more and more algorithms will be integrated into.

In the case of calculating the similarity of two genes, you can view the overlapped DAG of the terms annotated to each gene and the integrated DAG of these two.

2. Why GFSAT was born?


 Benefited from the high-throughput, large-scale experiments and statistics coupled with computer analysis methods, gene function studies have turned from single gene to gene network. In addition to protein-protein interaction network (PPIN), gene regulatory network (GRN), metabolic network (MN),signal transduction network (STN) and so on, a new gene network based on gene functional association has been born, namely Functional Gene network (GFN) , in which measuring the functional similarity of genes is the most critical part. GFSAT was born to do this based on GOA.

3. 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 ‚Äúintrinsic‚ÄĚ to GO with comparable performance.

4. 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.

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