Relation extraction is one of the core tasks of natural language processing, which aims to identify entities in unstructured text and judge the semantic relationships between them. In the traditional methods, the extraction of rich features and the judgment of complex semantic relations are inadequate. Therefore, in this paper, we propose a relation extraction model, HAGCN, based on heterogeneous graph convolutional neural network and graph attention mechanism. We have constructed two different types of nodes, words and relations, in a heterogeneous graph convolutional neural network, which are used to extract different semantic types and attributes and further extract contextual semantic representations. By incorporating the graph attention mechanism to distinguish the importance of different information, and the model has stronger representation ability. In addition, an information update mechanism is designed in the model. Relation extraction is performed after iteratively fusing the node semantic information to obtain a more comprehensive node representation. The experimental results show that the HAGCN model achieves good relation extraction performance, and its F1 value reaches 91.51% in the SemEval-2010 Task 8 dataset. In addition, the HAGCN model also has good results in the WebNLG dataset, verifying the generalization ability of the model.

Exploring changes in lipid metabolism is helpful for providing unique insight into hepatocellular carcinoma (HCC) pathogenesis mechanisms and early hepatocarcinogenesis. However, lipid metabolism involves different omics molecular interactions by means of both linear and nonlinear forms. Thus, we proposed a novel network construction method based on molecular pair evaluation from linear and nonlinear viewpoints (PELN) for clinical studies. In PELN, molecular relationships were explored in depth by horizontal comparison (linear relationship) and vertical comparison (nonlinear relationship) to reflect disease development for biomarker discovery. In the score calculated by PELN, case ratios and case frequencies were used to comprehensively measure the discriminative ability of the molecular pairs, which can reduce the influence of sampling variability resulting from different subjects. HCC genomics and metabolomics datasets related to lipid metabolism were analyzed by PELN, and the selected network warning signals were shown to effectively predict cancer onset. The experimental results showed that compared with other network methods, including DMNC, DNB-HC, ATSD-DN and MN-PCC, PELN was more robust and precise for distinguishing HCC samples from non-HCC samples. Further analysis using statistical methods demonstrated that studying changes in lipid metabolism using PELN based on multiomics data can help to further understand the pathological mechanisms associated with HCC development, contributing to early diagnosis and affecting clinical prognosis.

Due to the vigorous development of big data, news topic text classification has received extensive attention, and the accuracy of news topic text classification and the semantic analysis of text are worth us to explore. The semantic information contained in news topic text has an important impact on the classification results. Traditional text classification methods tend to default the text structure to the sequential linear structure, then classify by giving weight to words or according to the frequency value of words, while ignoring the semantic information in the text, which eventually leads to poor classification results. In order to solve the above problems, this paper proposes a BiLSTM-GCN (Bidirectional Long Short-Term Memory and Graph Convolutional Network) hybrid neural network text classification model based on dependency parsing. Firstly, we use BiLSTM to complete the extraction of feature vectors in the text; Then, we employ dependency parsing to strengthen the influence of words with semantic relationship, and obtain the global information of the text through GCN; Finally, aim to prevent the overfitting problem of the hybrid neural network which may be caused by too many network layers, we add a global average pooling layer. Our experimental results show that this method has a good performance on the THUCNews and SogouCS datasets, and the F-score reaches 91.37% and 91.76% respectively.

Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect simple biomedical events and complex events with the same model, and the performance of the complex biomedical event extraction is relatively low. In this paper, we build different neural networks for simple and complex events respectively, which helps to promote the performance of complex event extraction. To avoid redundant information, we design dynamic path planning strategy for argument detection. To take full use of the information between the trigger identification and argument detection subtasks, and reduce the cascading errors, we build a joint event extraction model. Experimental results demonstrate our approach achieves the best F-score on the biomedical benchmark MLEE dataset and outperforms the recent state-of-the-art methods.

Biomedical event extraction is an important branch of biomedical information extraction. Event detection is the most important subtask in event extraction, which has been widely concerned. Most of the existing research on event detection is based on traditional machine learning or neural network. However, they ignored the semantic information of the word itself and its event type and the insufficient features of the out-of-vocabulary neologism representation. In this paper, trigger extraction is treated as a sequence labeling problem. We propose a biomedical event detection model based on knowledge injection and model dual channel fine-tuning, which introduces an external biomedical knowledge base, UMLS. This architecture improves our model's ability to capture semantic information about the word itself and its event types, as well as information about out-of-vocabulary neologisms. The experimental results show that the proposed model improves the performance of biomedical event detection, and the F1 value on the MLEE dataset is 83.59%, which outperforms the recent state-of-the-art methods. Moreover, testing our model on GE13, the experimental results are also significantly improved.

During the development of complex diseases, there is a critical transition from one status to another at a tipping point, which can be an early indicator of disease deterioration. To effectively enhance the performance of early risk identification, a novel dynamic network construction algorithm for identifying early warning signals based on a data-driven approach (EWS-DDA) was proposed. In EWS-DDA, the shrunken centroid was introduced to measure dynamic expression changes in assumed pathway reactions during the progression of complex disease for network construction and to define early warning signals by means of a data-driven approach. We applied EWS-DDA to perform a comprehensive analysis of gene expression profiles of gastric cancer (GC) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Six crucial genes were selected as potential biomarkers for the early diagnosis of GC. The experimental results of statistical analysis and biological analysis suggested that the six genes play important roles in GC occurrence and development. Then, EWS-DDA was compared with other state-of-the-art network methods to validate its performance. The theoretical analysis and comparison results suggested that EWS-DDA has great potential for a more complete presentation of disease deterioration and effective extraction of early warning information.

BACKGROUND: Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low. RESULTS: In this paper, we propose a fine-grained Bidirectional Long Short Term Memory method for biomedical event extraction, which designs different argument detection models for simple and complex events respectively. In addition, multi-level attention is designed to improve the performance of complex event extraction, and sentence embeddings are integrated to obtain sentence level information which can resolve the ambiguities for some types of events. Our method achieves state-of-the-art performance on the commonly used dataset Multi-Level Event Extraction. CONCLUSIONS: The sentence embeddings enrich the global sentence-level information. The fine-grained argument detection model improves the performance of complex biomedical event extraction. Furthermore, the multi-level attention mechanism enhances the interactions among relevant arguments. The experimental results demonstrate the effectiveness of the proposed method for biomedical event extraction.

Extracting biomedical events from biomedical literature plays an important role in the field of biomedical text mining, and the trigger detection is a key step in biomedical event extraction. We propose a two-stage method for trigger detection, which divides trigger detection into recognition stage and classification stage, and different features are selected in each stage. In the first stage, we select the features which are more suitable for recognition, and in the second stage, the features that are more helpful to classification are adopted. Furthermore, we integrate word embeddings to represent words semantically and syntactically. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 79.75 percent, which outperforms the state-of-the-art systems.