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Motivation Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs. Results To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision–recall curves. Availability and implementation Code and data are available at: https://github.com/galaxysunwen/MSTE-master.

Error-Correcting Output Codes (ECOC) is widely deployed to tackle the multiclass classification problem by reducing the original multi-class problem to several binary sub-problems. This study attempts to design a dynamic ensemble selection strategy to promote the performance of ECOC algorithms. Concretely, each column in a coding matrix is matched with a set of feature subsets generated by various feature selection methods. In the decoding process, a novel criterion based on the data complexity theory is proposed to pick up an optimal feature subset from the candidate subsets, so as to better distinguish unknown samples. As this strategy can be embedded in all types of ECOC algorithms, seven classical ECOC algorithms are deployed to verify the effectiveness of our strategy. Experiments are carried out on a set of UCI data sets, and the results confirm that despite different working principle, the proposed strategy can further improve the performance of various ECOC algorithms in most cases. Our python source code is available at: https://github.com/MLDMXM2017/ECOC_DES .

Based on the conventional attentional encoder-decoder framework, multi-modal neural machine translation (NMT) further incorporates spatial visual features through a separate visual attention mechanism. In this aspect, most current multi-modal NMT models first separately learn the semantic representations of text and image and then independently produce two modalities of context vectors for word predictions, neglecting their semantic interactions. In this paper, we argue that learning text-image semantic interactions is more reasonable in the sense of jointly modeling two modalities for multi-modal NMT and propose a novel multi-modal NMT model with deep semantic interactions. Specifically, our model extends the conventional multi-modal NMT by introducing the following two attention neural networks: (1) a bi-directional attention network for modeling text and image representations, where the semantic representations of text are learned by referring to the image representations, and vice versa; (2) a co-attention network for refining text and image context vectors, which first summarizes the text into a context vector, then attends it to the image for obtaining the text-aware visual context vector. The final context vector is calculated by re-attending the visual context vector to the text. Results on the Multi30k dataset for different language pairs show that our model significantly improves on the state-of-the-art baselines. We have released our code at https://github.com/DeepLearnXMU/MNMT .

In conventional medical image printing methods, volumetric medical data needs to be conversed into STereo Lithography (STL) format, the most commonly used format for representing geometric models for 3D printing. However, this STL conversion process is not only time consuming, but more importantly, it often leads to the loss of accuracy. It has become a critical factor hindering the printing efficiency and precision of organ models. By examining the key characteristics of discrete medical volume data, this paper proposes a direct slicing technique for printing implicitly represented 3D medical models. The proposed method mainly consists of three algorithms: (1) A layer-based contour extraction algorithm for discrete volume data; (2) An inner shell construction algorithm based on discrete point differential indentation; (3) An infill generation algorithm based on the constructed virtual contour and scan lines. The proposed method has been applied to the slicing of several organ models for experiments, and the ratios of time cost and memory cost between the conventional method and the proposed method are about 4–100 and 1.1 to 1.4 respectively, which demonstrate that the proposed method has a great improvement in both time and space performance when compared with the conventional STL-based method. Our technique extends the direct input format of geometric models for additive manufacturing. That is, discrete volume data can be used as a direct input for additive manufacturing without conversion to STL format.

Scientific research journals have achieved the latest development in scientific research in various fields. However, the interpretation and use of biomedical information is still a very complicated issue. How to use practical methods to interpret biomedical literature into structured data and analyze it into what we can understand has become a major issue. In this paper, a frontier knowledge discovery model based on KOS and LDA is proposed and applied in detecting burst topic and its sematic information relationship in cancer field. Experiments showed that the model plays an important role in topic recognition, evolution recognition and visualization. Furthermore, the application of KOS combined with LDA can effectively remove noisy concept in sematic layer and show a good effect.

Alfalfa is known as “the king of forage”. It not only has high yield, but also has good quality. Studying the development process of alfalfa will help scholars in this field grasp the future research direction. In this paper, the data of alfalfa field from 2009 to August 14, 2020 retrieved on Web of Science is taken as the research object. Firstly, this paper analyses the current research status by using basic statistical methods. The selected analysis dimensions include time, country, funding source etc. Then, we conduct topic clustering research based on Latent Dirichlet Allocation (LDA) model and the scientometrics method to compare and analyze its interests from both the overall and time perspectives. Finally, the experimental results are presented in diagrams. The results reveal that the development of alfalfa field tends to be stable. China and the United States have made remarkable achievements in the field of alfalfa. The topics integration and decomposition trend is weak, and the research content has strong coherence.

The global pandemic of COVID-19 has brought huge public health challenges to the world. To meet the challenge, researchers worldwide have carried out a series of clinical studies. This article aims to analyze the progress of COVID-19, and explore the development and main research directions in 2020. The clinical trials focus on the design of the trial plan, which can be registered on the platform after the design is completed. The purpose of clinical publications is to publish trial results, focusing on in vitro tests, drug screening and so on. Based on these characteristics, this paper analyzes both clinical publications and clinical trials, and explores the development of global clinical research in 2020 from countries, intervention methods and trial designs. The experimental results show that the United States and China have published the most publications and carried out the most clinical trials. The maximum intervention methods in clinical trials & publications are focused on the drugs.

Clustering has many applications in data mining and machine learning. Fuzzy clustering methods have been widely used in clustering. However, fuzzy clustering methods still have a fatal problem: the cluster radius sensitivity problem. The cluster radius sensitivity problem means that clusters with smaller radius will predominate in clustering and obtain more data points. Aiming at this problem, we propose a fuzzy separation and shrinkage clustering algorithm (FSC). FSC uses cluster membership degrees and cluster sizes to construct a new membership distribution, and then moves the data points according to this new membership distribution. The accuracies of our algorithm on wine, iris, balance scale and seeds are as follows: 98.82%, 97.27%, 63.07% and 91.34%. Our contributions are: (1) We propose a fuzzy separation and shrinkage clustering algorithm, which can solve the cluster radius sensitivity problem. (2) The performance of our algorithm on the UCI datasets goes beyond the benchmark algorithms.

In order to obtain stable returns, this paper aims to establish a quantitative model with higher prediction accuracy. According to the time series characteristics of financial data, the prediction model with financial time series data is constructed by using the time-memory sequence model LSTM, which is applied to the representative SSE 50 series stocks. And based on this, the LSTM model with Encoder-Decoder mode and a hybridized framework of LSTM with CNN are built to improve the original model. Feature extraction is performed on the input data by using CNN, and then as an input to the LSTM, the extracted features are used for sequence prediction with the LSTM model.

In this paper, co-word analysis is used to analyze the evolvement in stem cell field. Articles in the stem cell journals are downloaded from PubMed for analysis. Terms selection is one of the most important steps in co-word analysis, so the useless and the general subject headings are removed firstly, and then the major subject headings and minor subject headings are weighted respectively. Then, improved information entropy is exploited to select the subject headings with the experts consulting. Hierarchical cluster analysis is used to cluster the subject headings and the strategic diagram is formed to analyze the evolutionary trends in the stem cell field.

Independent component analysis (ICA) has been widely deployed to the analysis of microarray datasets. Although it was pointed out that after ICA transformation, different independent components (ICs) are of different biological significance, the IC selection problem is still far from fully explored. In this paper, we propose a genetic algorithm (GA) based ensemble independent component selection (EICS) system. In this system, GA is applied to select a set of optimal IC subsets, which are then used to build diverse and accurate base classifiers. Finally, all base classifiers are combined with majority vote rule. To show the validity of the proposed method, we apply it to classify three DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that our ensemble method obtains stable and satisfying classification results when compared with several existing methods.