Background Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model. Results In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively. Conclusion The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.

To realize integration, organization and reusability of knowledge related to COVID-19, an ontology for COVID-19 (CIDO-COVID-19) was constructed which extended the Coronavirus Infectious Disease Ontology (CIDO) by adding terms of COVID-19 related to symptoms, prevention, drugs and clinical domains. First, terms from the existing ontologies, literature, clinical guidelines and other resources about COVID-19 were merged. Then, the Stanford seven-step approach was used to define and organize the acquired terms. Finally, the CIDO-COVID-19 was built on basis of the terms mentioned above using Proté gé . The CIDO-COVID-19 is a more comprehensive ontology for COVID-19, covering multiple areas in the domain of COVID-19, including disease, diagnosis, etiology, virus, transmission, symptom, treatment, drug and prevention.

Query expansion (QE) has been widely used in electronic medical record (EMR) retrieval for assisted diagnosis and clinical research. However, existing QE algorithms haven't achieved satisfactory performance in Chinese EMR retrieval, and one noticeable problem is that the weights of expansion terms and retrieval scores have unreasonable factors for lack of the solid consideration of clinical needs. Here we propose an algorithm of QE for Chinese EMR retrieval by improving expansion term weights and retrieval scores. First, the weights of expansion terms are assigned with semantic similarities, category weights and co-occurrence frequencies between expansion terms and multiple query terms. Then the retrieval scores calculated by expansion terms are limited to reduce the query drift caused by high-frequency expansion terms. Experiment results show that our method gets a 33.3% increase in the precision at top 10, a 90.4% increase in the recall, and a 13.2% increase in MAP compared with four baselines. It proves that our improvement scheme can ensure the accuracy of expansion term weights and decrease the query drift caused by QE, which substantially improves the performance of Chinese EMR retrieval.

Nanocomposites composed by polymeric matrix with micro/nano fillers have drawn lots of attention since their dramatic properties beyond pristine polymers.