Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.Comment: Published on IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 pages, 5 figure

Correction to: Mining a stroke knowledge graph from literature

From Springer Nature via Jisc Publications RouterHistory: registration 2021-11-30, collection 2021-12, pub-electronic 2021-12-08, online 2021-12-08Publication status: Publishe

Mining a stroke knowledge graph from literature

From Springer Nature via Jisc Publications RouterHistory: collection 2021-05, received 2021-06-13, accepted 2021-07-06, registration 2021-07-09, pub-electronic 2021-07-29, online 2021-07-29Publication status: PublishedFunder: National High-level Personnel for Defense Technology Program; Grant(s): (2017-JCJQ-ZQ-013), and NSF 61902405Funder: the national key r&d project by ministry of science and technology of china; Grant(s): 2018YFB1003203Funder: the open fund from the State Key Laboratory of High Performance Computing; Grant(s): No. 201901-11Funder: National Science Foundation of China; Grant(s): U1811462Abstract: Background: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the “Western” biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases. Results: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine. Conclusions: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Strok

HFN : Heterogeneous feature network for multivariate time series anomaly detection

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies

RNA-Seq reveals the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango (Mangifera indica L.) peel

IntroductionFlavonoids are important water soluble secondary metabolites in plants, and light is one of the most essential environmental factors regulating flavonoids biosynthesis. In the previous study, we found bagging treatment significantly inhibited the accumulation of flavonols and anthocyanins but promoted the proanthocyanidins accumulation in the fruit peel of mango (Mangifera indica L.) cultivar ‘Sensation’, while the relevant molecular mechanism is still unknown.MethodsIn this study, RNA-seq was conducted to identify the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango peel.ResultsBy weighted gene co-expression network analysis (WGCNA), 16 flavonoids biosynthetic genes were crucial for different flavonoids compositions biosynthesis under bagging treatment in mango. The higher expression level of LAR (mango026327) in bagged samples might be the reason why light inhibits proanthocyanidins accumulation in mango peel. The reported MYB positively regulating anthocyanins biosynthesis in mango, MiMYB1, has also been identified by WGCNA in this study. Apart from MYB and bHLH, ERF, WRKY and bZIP were the three most important transcription factors (TFs) involved in the light-regulated flavonoids biosynthesis in mango, with both activators and repressors. Surprisingly, two HY5 transcripts, which are usually induced by light, showed higher expression level in bagged samples.DiscussionOur results provide new insights of the regulatory effect of light on the flavonoids biosynthesis in mango fruit peel

Electrically tuned whispering gallery modes microresonator based on microstructured optical fibers infiltrated with dual-frequency liquid crystals

An electrically tunable whispering gallery mode (WGM) microresonator based on an HF-etched microstructured optical fiber (MOF) infiltrated with dual-frequency liquid crystals (DFLCs) is proposed and experimentally demonstrated for the investigation of the crossover frequency and Freedericksz transition of DFLCs. Experimental results indicate that for applied electric field with operation frequency below the crossover frequency, WGM resonance wavelength decreases with the increment of applied electric field strength. On the contrary, for applied electric field with operation frequency beyond the crossover frequency, WGM resonance dips show red shift as the applied electric field intensity increases. The proposed electrically tunable microcavity integrated with DFLCs is anticipated to find potential applications in optical filtering, all-optical switching, and electrically manipulated bi-directional micro-optics devices

Tuning of polarization-dependent whispering gallery modes in grapefruit microstructured optical fibers infiltrated with negative dielectric anisotropy liquid crystals

An electrically tunable whispering gallery mode (WGM) microresonator based on microstructured optical fibers (MOFs) infiltrated with negative dielectric anisotropy liquid crystals (LCs) is proposed and experimentally demonstrated. Experimental results indicate that the second radial order mode of the MOF microresonator has stronger electric field response than the first radial order mode and the resonance dip for TE polarization mode is more sensitive to the applied electric field intensity in comparison with the TM polarization mode resonance dip. The Freedericksz transition threshold of the proposed MOF microresonator is experimentally found to be about 2.0 V/um. The electrically tunable microresonator integrated with negative dielectric anisotropy LCs is anticipated to find potential applications in optical filtering, all-optical switching, and electrically controlled micro-optics devices