Gene Semantic Similarity Neural Network
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A Gene Semantic Similarity Neural Network is a Semantic Similarity Neural Network in which nodes represent genes (or proteins) while edges represent the semantic similarity among them.
- AKA: Gene Ontology Based Semantic Similarity Network.
- Example(s):
- a Gene Semantic Similarity Network for Identifying Disease Genes (e.g. Tian et al., 2017; Jiang et al., 2011),
- a Gene Semantic Similarity Network for Identifying Protein Complexes (Wang et al., 2012),
- a lncRNA Functional Semantic Similarity Neural Network (Chen et al., 2015)
- a UFO Semantic Similarity Network (Le et al., 2020),
- …
- Counter-Example(s):
- See: Gene Ontology, Human Phenotype Ontology, Disease Ontology, Semantic Similarity Neural Network, Artificial Neural Network, Deep Learning Neural Network, Natural Language Processing Task, Costumer Care Chat System.
References
2020
- (Le, 2020) ⇒ Duc-Hau Le (2020). "UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization". In: PloS one, 15(7), e0235670.
2017
- (Tian et al., 2017) ⇒ Zhen Tian, Maozu Guo, Chunyu Wang, LinLin Xing, Lei Wang, and Yin Zhang (2017). "Constructing an integrated gene similarity network for the identification of disease genes". In: Journal of biomedical semantics, 8(1), 27-41.
2015
- (Chen et al., 2015) ⇒ Xing Chen, Chenggang Clarence Yan, Cai Luo, Wen Ji, Yongdong Zhang, and Qionghai Dai (2015). "Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity". In: Scientific Reports volume 5, Article number: 11338 (2015).
2013
- (Guzzi et al.,2013) ⇒ Pietro Hiram Guzzi, Pierangelo Veltri, and Mario Cannataro (2013). "Thresholding of Semantic Similarity Networks Using a Spectral Graph-Based Technique". In: Proceedings of the Second International Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2013/ECML-PKDD 2013).
- QUOTE: SSNs are edge-weighted graphs where the nodes are concepts (e.g. proteins) and each edge has an associated weight that represents the semantic similarity among related pairs of nodes(...)
As introduced, in a Semantic Similarity Networks, nodes represent proteins or genes, and edges represent the value of similarity among them. Starting from a dataset of genes or proteins, a SSN may be built in an iterative way, and once built, algorithms from graph theory may be used to extract topological properties that encode biological knowledge.
- QUOTE: SSNs are edge-weighted graphs where the nodes are concepts (e.g. proteins) and each edge has an associated weight that represents the semantic similarity among related pairs of nodes(...)
2012
- (Wang et al., 2012) ⇒ Jian Wang, Dong Xie, Hongfei Lin, Zhihao Yang, and Yijia Zhang (2012). "Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks". In: Proteome science (Vol. 10, No. 1, pp. 1-10). BioMed Central.
2011
- (Jiang et al., 2011) ⇒ Rui Jiang, Mingxin Gan, and Peng He (2011). "Constructing a Gene Semantic Similarity Network for the Inference of Disease Genes". In: BMC Systems Biology, 5(S-2). DOI:10.1186/1752-0509-5-S2-S2.
- QUOTE: The procedure of constructing a gene semantic similarity network is illustrated in Figure 1. First, we calculate pairwise semantic similarity scores for GO terms in the biological process domain, obtaining a matrix that contains semantic similarity scores between GO terms. Next, we calculate pairwise semantic similarity scores for human genes using similarity scores of GO terms and annotations of genes, obtaining a matrix that contains semantic similarity scores between genes. Then, we filter out low similarity values in this matrix by keeping only the first $k$ nearest neighbors for each gene and assigning zeros to all other elements. Finally, we obtain a gene semantic similarity network by treating non-zero elements in the resulting matrix as weights of edges between corresponding genes.