Entity extraction and graph construction based on Chinese medical text / 中国药科大学学报

@#Knowledge graph technology has promoted the progress of new drug research and development, but domestic research starts late and domain knowledge is mostly stored in text, resulting in low rate of knowledge graph reuse.Based on multi-source and heterogeneous medical texts, this paper designed a Chinese named entity recognition model based on Bert-wwm-ext pre-training model and also integrated cascade thought, which reduced the complexity of traditional single classification and further improved the efficiency of text recognition.The experimental results showed that the model achieved the best performance with an F1-score of 0.903, a precision of 89.2%, and a recall rate of 91.5% on the self-built dataset.At the same time, the model was applied to the public dataset CCKS2019, and the results showed that the model had better performance and recognition effect.Using this model, this paper constructed a Chinese medical knowledge graph, involving 13 530 entities, 10 939 attributes and 39 247 relationships of them in total.The Chinese medical entity extraction and graph construction method proposed in this paper is expected to help researchers accelerate the new discovery of medical knowledge, and shorten the process of new drug discovery.

Drug repurposing for Alzheimer''s disease using knowledge graph embedding / 中国药科大学学报

@#Alzheimer''s disease (AD) has brought to us huge medical and economic burdens, and so discovery of its therapeutic drugs is of great significance.In this paper, we utilized knowledge graph embedding (KGE) models to explore drug repurposing for AD on the publicly available drug repurposing knowledge graph (DRKG).Specifically, we applied four KGE models, namely TransE, DistMult, ComplEx, and RotatE, to learn the embedding vectors of entities and relations on DRKG.By using three classical knowledge graph evaluation metrics, we then evaluated and compared the performance of these models as well as the quality of the learned embedded vectors.Based on our results, we selected the RotatE model for link prediction and identified 16 drugs that might be repurposed for the treatment of AD.Previous studies have confirmed the potential therapeutic effects of 12 drugs against AD, i.e., glutathione, haloperidol, capsaicin, quercetin, estradiol, glucose, disulfire, adenosine, paroxetine, paclitaxel, glybride and amitriptyline.Our study demonstrates that drug repurposing based on KGE may provide new ideas and methods for AD drug discovery.Moreover, the RotatE model effectively integrates multi-source information of DRKG, enabling promising AD drug repurposing.The source code of this paper is available at https://github.com/LuYF-Lemon-love/AD-KGE.

Prediction of plasma protein binding rate based on machine learning / 中国药科大学学报

@#Predicting the protein binding rate of drugs in plasma is helpful to us in understanding the pharmacokinetic characteristics of drugs, with much value of reference for early research on drug discovery. In this study, plasma protein binding rate information of 2 452 clinical drugs were collected.Two pieces of software, Molecular Operating Environment (MOE) and Mordred, were used to calculate molecular descriptors, which were used as input features of the model.Extreme gradient boosting (XGBoost) algorithm and random forest (RF) algorithm were then used to build a machine learning model.The results showed that, compared with MOE, the prediction performance of the constructed model was better using the molecular descriptor calculated by Mordred as the input of the model.The prediction performance results of the model constructed using the XGBoost algorithm and the RF algorithm were similar, and the R2 of the optimal model were both 0.715.According to the research results, it can be concluded that the drug plasma protein binding rate is closely related to some physical and chemical properties of the drug molecule, such as water solubility, octanol/water partition coefficient and conjugated double bonds.Using these parameters to predict the plasma protein binding rate of drugs has the advantages of convenience and efficiency, which can provide reference for related pharmacokinetic studies.

Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis / 生物医学工程学杂志

The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn't during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.