IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method

Similarity computing of ontological concept has made rapid progress in the field of data mining, information processing and artificial intelligence and becoming one of the hot research field of information technology, particularly the idea of the semantic Web was proposed in 2000, the concept of semantic similarity has gotten more attention, while also facilitating its further development and application in information retrieval. Considering the deficiencies of existing concept similarity algorithm, this paper design the method to reduce the candidate set of domain concept, and put forward a similarity calculation model based on the concept name, instances, properties, and semantic structure of domain ontology. Integrated several main influencing factors, the experiments show the proposed algorithm can express the impact of various factors on the similarity in the calculation concept similarity of domain ontology. By comparing with the traditional similarity method and expertise experience value, the experiment result shows that the effectiveness and correctness of the concept similarity calculation model.

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The semantic similarity of the geo-information concept calculation model based on ontology

2010 18th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2010.5567548 ◽

2010 ◽

Author(s):

Kai Su ◽

Hong Wang ◽

Fan Pu

Keyword(s):

Semantic Similarity ◽

Calculation Model ◽

Model Based ◽

Information Concept

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Chinese Text Similarity Calculation Model Based on Multi-Attention Siamese Bi-LSTM

10.1145/3494885.3494902 ◽

2021 ◽

Author(s):

Zhongguo Wang ◽

Bao Zhang

Keyword(s):

Chinese Text ◽

Calculation Model ◽

Text Similarity ◽

Model Based ◽

Similarity Calculation

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FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model

Oncotarget ◽

10.18632/oncotarget.10008 ◽

2016 ◽

Vol 7 (29) ◽

pp. 45948-45958 ◽

Cited By ~ 75

Author(s):

Xing Chen ◽

Yu-An Huang ◽

Xue-Song Wang ◽

Zhu-Hong You ◽

Keith C.C. Chan

Keyword(s):

Calculation Model ◽

Fuzzy Measure ◽

Functional Similarity ◽

Similarity Calculation

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A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction

Molecular BioSystems ◽

10.1039/c6mb00853d ◽

2017 ◽

Vol 13 (6) ◽

pp. 1202-1212 ◽

Cited By ~ 30

Author(s):

Xing Chen ◽

Zhi-Chao Jiang ◽

Di Xie ◽

De-Shuang Huang ◽

Qi Zhao ◽

...

Keyword(s):

Computational Model ◽

Semantic Similarity ◽

Computational Models ◽

Disease Association ◽

Functional Similarity ◽

Model Based ◽

Disease Associations

Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction (SDMMDA) to predict potential miRNA–disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs.

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Traditional Chinese Medicine Text Similarity Calculation Model Based on the Bidirectional Temporal Siamese Network

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/2337924 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jigen Luo ◽

Wangping Xiong ◽

Jianqiang Du ◽

Yingfeng Liu ◽

Jianwen Li ◽

...

Keyword(s):

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Knowledge Integration ◽

Short Term Memory ◽

Calculation Model ◽

Word Segmentation ◽

Text Similarity ◽

Model Based ◽

Siamese Network ◽

Similarity Calculation

The text similarity calculation plays a crucial role as the core work of artificial intelligence commercial applications such as traditional Chinese medicine (TCM) auxiliary diagnosis, intelligent question and answer, and prescription recommendation. However, TCM texts have problems such as short sentence expression, inaccurate word segmentation, strong semantic relevance, high feature dimension, and sparseness. This study comprehensively considers the temporal information of sentence context and proposes a TCM text similarity calculation model based on the bidirectional temporal Siamese network (BTSN). We used the enhanced representation through knowledge integration (ERNIE) pretrained language model to train character vectors instead of word vectors and solved the problem of inaccurate word segmentation in TCM. In the Siamese network, the traditional fully connected neural network was replaced by a deep bidirectional long short-term memory (BLSTM) to capture the contextual semantics of the current word information. The improved similarity BLSTM was used to map the sentence that is to be tested into two sets of low-dimensional numerical vectors. Then, we performed similarity calculation training. Experiments on the two datasets of financial and TCM show that the performance of the BTSN model in this study was better than that of other similarity calculation models. When the number of layers of the BLSTM reached 6 layers, the accuracy of the model was the highest. This verifies that the text similarity calculation model proposed in this study has high engineering value.

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