A BERT-Span model for Chinese named entity recognition in rehabilitation medicine

Background Due to various factors such as the increasing aging of the population and the upgrading of people’s health consumption needs, the demand group for rehabilitation medical care is expanding. Currently, China’s rehabilitation medical care encounters several challenges, such as inadequate awareness and a scarcity of skilled professionals. Enhancing public awareness about rehabilitation and improving the quality of rehabilitation services are particularly crucial. Named entity recognition is an essential first step in information processing as it enables the automated extraction of rehabilitation medical entities. These entities play a crucial role in subsequent tasks, including information decision systems and the construction of medical knowledge graphs. Methods In order to accomplish this objective, we construct the BERT-Span model to complete the Chinese rehabilitation medicine named entity recognition task. First, we collect rehabilitation information from multiple sources to build a corpus in the field of rehabilitation medicine, and fine-tune Bidirectional Encoder Representation from Transformers (BERT) with the rehabilitation medicine corpus. For the rehabilitation medicine corpus, we use BERT to extract the feature vectors of rehabilitation medicine entities in the text, and use the span model to complete the annotation of rehabilitation medicine entities. Result Compared to existing baseline models, our model achieved the highest F1 value for the named entity recognition task in the rehabilitation medicine corpus. The experimental results demonstrate that our method outperforms in recognizing both long medical entities and nested medical entities in rehabilitation medical texts. Conclusion The BERT-Span model can effectively identify and extract entity knowledge in the field of rehabilitation medicine in China, which supports the construction of the knowledge graph of rehabilitation medicine and the development of the decision-making system of rehabilitation medicine

Additional file 2 of Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

Additional file 2: Table S1. Genome-wide eQTLs identified at 20 DAF. Table S2. Genome-wide eQTLs identified at 40 DAF. Table S3. Genome-wide eGene-eQTLs identified at 20 DAF. Table S4. Genome-wide eGene-eQTLs identified at 40 DAF. Table S5. Results of GO enrichment analysis of specifically identified eGenes at 20 DAF. Table S6. Results of GO enrichment analysis of specifically identified eGenes at 40 DAF. Table S7. Summary of ATAC-Seq data. Table S8. List of homoeologous genes among subgenomes. Table S9. List of homoeologous genes on gene expression trend and regulation at 20 DAF. Table S10. List of homoeologous genes on gene expression trend and regulation at 40 DAF. Table S11. The information of hotspots at 20 DAF. Table S12. The information of hotspots at 40 DAF. Table S13. The list of TWAS significant genes of SOC and SGC at 20 DAF. Table S14. The list of TWAS significant genes of SOC and SGC at 40 DAF. Table S15. Enrichment analysis of eQTL hotspot regulatory genes in TWAS significant genes of SOC at 20 DAF. Table S16. Enrichment analysis of eQTL hotspot regulatory genes in TWAS significant genes of SOC at 40 DAF. Table S17. The results of Tomtom analysis of key sequences identified by Basenji module. Table S18. Primers for gene cloning and PCR confirmation

Additional file 1 of Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

Additional file 1: Fig. S1. Overview of experimental and research analysis methods. Fig. S2. Venn diagram of the distribution of genes regulated by different types of eQTLs (local eQTL and distant eQTL) at 20 DAF and 40 DAF. (a) Distribution number of genes which were regulated by different types of eQTLs at 20 DAF. (b) Distribution number of genes which were regulated by different types of eQTLs at 40 DAF. Fig. S3. Manhattan plot of BnaA05.FAD7 eGWAS at 40 DAF. Fig. S4. Study design on ATAC-seq of 6 representative accessions of B. napus. Fig. S5. Correlation analysis of 59 ATAC-seq samples. The samples are named according to “(22, 26, 34 or 40) DAF” + “accession_cellular ploidy (2C, 3C or 4C)” + “biological replicate” format naming. Fig. S6. Regional plot of ATAC-seq data and eGWAS results of BnaA08.TGD1. BnaA08.TGD1 is marked by a dashed line. The shaded area indicates the lead SNP of the local eQTL affecting BnaA08.TGD1. Fig. S7. Comparison of the explained variance (r2) of eQTLs for expression variation in or not in OCRs. Fig. S8. Expression correlation analysis of adjacent genes and randomly sampled gene pairs. Violin plot shows that the expression correlation of adjacent genes is significantly higher than that of randomly sampled gene pairs, *** indicates P 0.05, * indicates P < 0.05, ** indicates P < 0.01 and *** indicates P < 0.001 compared with WT in Student’s t test. Fig. S34. Transcriptional regulation of genes are activated by BnaA09.SCL31 or BnaA07.NAC13. a Schematic representation of the constructs used for the dual-luciferase assay. The effector constructs contain BnaA09.SCL31 and BnaA07.NAC13 driven by the CaMV35S promoter, respectively. The reporter construct contains the firefly luciferase driven by BnaA07.NAC13, BnaC07.LPAAT, BnaC03.SLP1 and BnaA07.SRO3 promoter, and the Renilla luciferase (REN) driven by the CaMV35S promoter. And the black square is the terminator. b Bar graph showing the relative LUC/REN ratio in the dual-luciferase assay. Values are means(SD) (n = 3 biological repeats). BnaA07.SRO3 (SIMILAR TO RCD ONE 3)

Additional file 3 of Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

Additional file 3. Review history