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A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.

The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-the-art algorithms.

The ATC (Anatomical Therapeutic Chemical) code of a drug is a classification system designated by the World Health Organization Collaborating Center for Drug Statistics Methodology. Correctly identifying the potential ATC codes for drugs can accelerate drug development and reduce the cost of experiments. Several classifiers have been proposed in this regard. However, they lack of ability to learn basic features from sparsely known drug-ATC code associations. In this paper, we provide a novel end-to-end model, so-called RNPredATC, to predict potential drug-ATC code associations in five ATC classification levels. RNPredATC can extract dense feature vectors from sparsely known drug-ATC code associations and reduce the impact from the degradation problem by a novel deep residual learning. We extensively compare our method with some state-of-the-art methods, including NetPredATC, SPACE, and some multi-label-based methods. Our experimental results show that RNPredATC achieves better performances in five-fold and ten-fold cross validations. Furthermore, the visualization analysis of hidden layers and case studies of predicted associations at the fifth ATC classification level confirm that RNPredATC can effectively identify the potential ATC codes of drugs.

With the rapid development of computer technology and the needs of modern higher education reform, computer network multimedia technology has begun to be applied more and more to the application of higher algebra teaching reform in universities. This article mainly analyzes the advantages of using computer network multimedia technology in the reform of advanced algebra teaching, as well as the problems that should be paid attention to when applying it to network multimedia in the teaching process, and puts forward the corresponding problems in how to use its network multimedia to carry out advanced algebraic mathematics reform Suggestions. This article mainly analyzes the advantages of using computer network multimedia technology in the reform of advanced algebra teaching, as well as the problems that should be paid attention to when applying it to network multimedia in the teaching process. The research of this article is dedicated to the deeper understanding of mathematics teachers in colleges and universities. Network multimedia technology the theoretical basis and substantive understanding integrated in the teaching of advanced algebra. Understand the various factors that hinder the effective integration of network multimedia technology and course teaching, and truly realize the real and effective integration of multimedia technology into the reform of higher algebra teaching, and comprehensively improve the quality of teaching. By mainly expounding the time that network multimedia technology is used in the process of advanced algebra teaching, different cases of multimedia teaching are given for analysis from the perspectives of new courses, review courses, research-based learning, and elective courses. Experimental research results show that the application of computer network multimedia technology in the reform of higher algebra teaching is to meet the needs of the development of the times, but the better application of this technology should be combined with the development of the school itself. Teachers continue to understand the teaching skills of network multimedia and give full play to its role.

Various microbes have proved to be closely related to the pathogenesis of human diseases. While many computational methods for predicting human microbe-disease associations (MDAs) have been developed, few systematic reviews on these methods have been reported. In this study, we provide a comprehensive overview of the existing methods. Firstly, we introduce the data used in existing MDA prediction methods. Secondly, we classify those methods into different categories by their nature and describe their algorithms and strategies in detail. Next, experimental evaluations are conducted on representative methods using different similarity data and calculation methods to compare their prediction performances. Based on the principles of computational methods and experimental results, we discuss the advantages and disadvantages of those methods and propose suggestions for the improvement of prediction performances. Considering the problems of the MDA prediction at present stage, we discuss future work from three perspectives including data, methods and formulations at the end.

Background A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. Result In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. Conlusion In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.

Shared e-bikes are popular because of their green, eco-friendly and efficient features. Due to the limited battery capacity of the e-bikes, the energy problem has become one of the main factors limiting its further development. The energy problem can be solved by using static charging piles (SCP) to replenish the batteries of shared e-bike. The location of the shared e-bike is time-varying, resulting in the optimal deployment of SCP as a complex location problem. In this paper, we propose an optimal Deployment algorithm for Maximum Coverage combined the Dynamic Changes of nodes (max-DCDC) based on the known number of SCP. This method first quantitatively analyzes the dynamic change process of the shared e-bike to reduce the deployment scope of the SCP. Then, according to the geometric characteristics of the e-bike distribution within the deployment scope to optimizes the deployment location of the SCP. Simulation experiments show that max-DCDC has better performance in terms of deployment stability and e-bike coverage compared with the other algorithms.

The wide applications of automatic disease inference in many medical fields improve the efficiency of medical treatments. Many efforts have been made to predict patients' future health conditions according to their full clinical texts, clinical measurements or medical codes. Symptoms reflect the onset of diseases and can provide credible information for disease diagnosis. In this study, we propose a new disease inference method by extracting symptoms and integrating two symptom representation approaches. To reduce the uncertainty and irregularity of symptom descriptions in Electronic Medical Records (EMR), a comprehensive clinical knowledge database consisting of massive amount of data about diseases, symptoms, and their relationships, we extract symptoms with existing nature language process tool Metamap which is designed for biomedical texts. To take advantages of the complex relationship between symptoms and diseases to enhance the accuracy of disease inference, we present two symptom representation models: term frequency-inverse document frequency (TF-IDF) model for the representation of the relationship between symptoms and diseases and Word2Vec for the expression of the semantic relationship between symptoms. Based on these two symptom representations, we employ the bidirectional Long Short Term Memory networks (BiLSTMs) to model symptom sequences in EMR. Our proposed model shows a significant improvement in term of AUC (0.895) and F1 (0.572) for 50 diseases in MIMIC-III dataset. The results illustrate that the model with the combination of the two symptom representations perform better than the one with only one of them.

A drug-drug interaction(DDI) was defined as the pharmacological effect(s) of a drug influenced by another drug. The positive DDIs can improve the therapeutic effect of patients. However, the negative DDIs can lead serious results, such as drug withdrawal from market and even patient death. Currently, multiple pharmaceutical drugs have widely been used to treat complex diseases, such as cancer. The traditional biomedical experiments are very time-consuming and very costly to validate new DDIs. Therefore, it is appealing to develop computational methods to discover potential DDIs. In this study, we propose a new computational method (called IDNDDI) to predict novel DDIs. Based on the binary vector of drug chemical, biological and phenotype data, IDNDDI computes the integrated drug feature similarity by the cosine similarity method. In addition, the node-based drug network diffusion method is used to calculate the relational initial scores for new drugs. To systematically evaluate the prediction performance of IDNDDI and compare it with other prediction methods, we conduct the 5-fold cross validation and de novo drug validation. In terms of the AUC (area under the ROC curve)value, IDNDDI achieves the better prediction performance in the 5-fold cross validation, specifically, the AUC value is 0.9691, which is larger than the state-of-the-art L1E (L1 Classifier ensemble method) results of 0.9570. In addition, IDNDDI also obtains the best prediction result in the de novo drug validation and the AUC reaches 0.9292. The prediction ability in application of our method is also illustrated by case studies. IDNDDI is an effective DDI prediction method which can help to reduce adverse drug reactions and improve the efficiency of drug development progress.

Identifying influential nodes in complex networks is still an open issue. A number of measures have been proposed to improve the validity and accuracy of the influential nodes in complex networks. In this paper, we propose a new method, called GLS, to identify influential nodes. This method aims to determine the influence of the nodes themselves, while combining the structural characteristics of the network. This method considers not only the local structure of the network but also its global structure. The influence of the global structure is measured by its closeness to all other nodes in the network, but the influence of local structures only considers the influence contribution of the nearest neighbor nodes. To evaluate the performance of GLS, we use the Susceptible-Infected-Recovered (SIR) model to examine the spreading efficiency of each node, and compare GLS with PageRank, Hyperlink Induced Topic Search (Hits), K-shell, H-index, eigenvector centrality (EC), closeness centrality (CC), ProfitLeader, betweenness centrality (BC) and Weighted Formal Concept Analysis (WFCA) on 8 real-world networks. The experimental results show that GLS can rank the spreading ability of nodes more accurately and more efficiently than other methods.

Automatic disease inference is an important task in many medical applications. Many efforts have been made to predict patients' future health according to their full clinical texts, clinical measurements or medical codes. Symptoms reflect the onset of diseases and can provide credible information for diseases diagnosis. In this paper, we propose a new disease inference method by extracting symptoms. To reduce the uncertainty and irregularity of symptom descriptions, we use MetaMap to extract symptoms from the Electronic Medical Records (EMR), a comprehensive clinical knowledge database consisting of massive amount of data about diseases, symptoms, and their relationships. To take advantage of the complex relationship between symptoms and diseases to enhance the accuracy of disease inference, we present symptom representation model: term frequency-inverse document frequency (TF-IDF) for the representation of the relationship between symptoms and diseases. Based on the symptom representation, we employ bidirectional recurrent neural network (Bi-LSTM) to model symptom sequence in EMR. Our proposed model shows a significant improvement in disease inference, i.e., 0.853 AUC and 0.563 F1 for 50 diseases on MIMIC-III dataset.

Under the environment of the large educational data, the search results couldn't meet the requirements of the resource access if the users' intentions are unclear. Therefore, it is urgent to solve the accuracy problem for educational resource search. At the same time, since people pay more attention to privacy issues, it becomes a stumbling block to the development of personalized search. In order to solve this problem, we propose a Personalized Search with Secure Updates (PSSU) in educational resources. The PSSU scheme is able to obtain large educational resources data from resources contributed by the general public by the introduction of Trusted Third-Party (TTP) server, and evaluate the resources by analyzing the user's usage, then the users can obtain high-quality resources that meet their own needs. Since the user's search and index update are performed in an encrypted environment, the user's privacy will not be revealed.

With the development of the internet of vehicles, the safety of the wireless network communication in the internet of vehicles and the privacy protection of users are in need of an immediate solution. In order to address the interception and tampering by an attacker of the contents among the vehicle mobile terminals, and their consequence of the privacy leakage of the location, and the user’s activity tracks and also the consequence of the user’s receiving the wrong information due to the tampering of the message, this essay comes up with a solution of tamper-proof ciphertext of the revised ciphertext policy attribute-based encryption, which to some degree has reduced the risk of the leakage of the user’s information. This solution realize the encryption protection of the contents of the vehicle mobile terminals by destroying the structure extension of the ciphertext, achieves the goal of non-extensibility security.

Understanding disease-disease associations can not only help us gain deeper insights into complex diseases, but also lead to improvements in disease diagnosis, drug repositioning and new drug development. Due to the growing body of high-throughput biological data, a number of methods have been proposed for the computation of similarity among diseases during past decades. Recently, the disease module theory has been presented, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of protein-protein interaction network. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and protein-protein interaction network data based on disease module theory. By considering the interactions between disease modules and each module’s modularity, ModuleSim outperforms other four popular methods for predicting disease-disease similarity.

Semantic features are critical intelligence information for mobile ubiquitous multimedia, how to manage and retrieve the semantic information has been an important issue. In this paper, a novel semantic retrieval approach named Data Hiding based Semantic Retrieval (DHSR) for ubiquitous multimedia is proposed. This approach consists of the following features: (1) Every multimedia document has to be semantically annotated by several users before saved into multimedia database. (2) Semantic information described by object ontology will be hidden in the multimedia document data. (3) Semantic information will not be lost even if the multimedia document is copied, cut or leave the database. Our work provides a search engine with convenient user interfaces. The experimental results show that DHSR can search the multimedia documents reflecting users' query intent more effectively compared with some traditional approaches.

To protect users’ privacy and provide efficient and secure key management in Vehicular ad hoc networks (VANETs), this paper proposes a novel Efficient Dynamic Key Management scheme based on Dynamic Secret Sharing, namely EDKM-DSS. The proposed scheme replaces the complex group signature technology with the elliptic curve ElGamal threshold mechanism and the dynamic secret sharing technology, and realizes the key management of the group communication. Experiments results show that the proposed novel scheme can effectively improve the overall performance.

Wireless sensor networks (WSNs) are exploiting their numerous appli- cations in both military and civil fields. For most WSNs applications, it is important to guarantee high security of the deployed network in order to de- fend against attacks from adversaries. In this chapter, we survey the recent progress in the security issues for wireless sensor networks, mainly focusing on the key distribution and management schemes and some high layer pro- tocols such as secure routing, location privacy protection and secure data aggregation. Representative works on each topic are described in detail and both of their strongpoint and drawbacks are discussed, based on which we give some direction for future research.

With the development of Internet applications, a number of anonymous communication systems have been realized to protect the identity of communication participants. Therefore, it is essential to give a theoretically based and practically usable objective numerical measure for the provided level of anonymity. In this paper some typical anonymity measures are analyzed and limitations of these measures was highlighted. Then a new anonymity measure based on partial entropy is proposed, in which the anonymity is measured by using the entropy of the probability distribution of some distinct subjects in anonymity set. The results of analysis and calculation show that the new measure is preferable for anonymity evaluation.