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Today, the rapid growth of vehicles connected to the Internet enables provides various services to consumers, including traffic management, traffic safety, and entertainment. Vehicular Ad-Hoc Network (VANET) is one of the most prominent and emerging technologies on the Internet of Vehicular Things (IoVT). This technology offers to fulfill requirements such as robust information exchange, and infotainment among vehicles for the smart city environment. Still, it has some challenges such as centralization, storage, security, and privacy because all city vehicular networks send vehicles and road-related information data directly to the cloud. This article proposes BIIoVT: Blockchain-based Secure Storage Architecture for Intelligent Internet of Vehicular Things (IIVoT) to mitigate the above-mention issues. Blockchain provides security and privacy at each city's vehicular networks and decentralized storage at the cloud layer with a Distributed Hash Table (DHT). It also examines how the vehicular network offers a secure platform. The validation results of the proposed architecture show an outstanding balance of secure storage and efficiency for the IoVT compared to existing methods.

Model compression is a promising approach for reducing the model size of pretrained-language-models (PLMs) on low resource edge devices and applications. Unfortunately, the compression process always accompanies a cost of performance degradation, especially for the low resource downstream tasks, i.e., multiple-choice question answering. To address the degradation issue of model compression on PLMs, we proposed an end-to-end reptile (ETER) meta-learning approach to improving the performance of PLMs on the low resource multiple-choice question answering task. Specifically, our ETER improves the traditional two-stage meta-learning to an end-to-end manner, integrating the target finetuning stage into the meta training stage. To strengthen the generic meta-learning, ETER employs two-level meta-task construction from instance-level and domain-level to enrich its task generalization. What is more, ETER optimizes meta-learning by parameter constraints to reduce its parameter learning space. Experiments demonstrate that ETER significantly improved the performance of compressed PLMs and achieved large superiority over the baselines on different datasets.

Password is one of the most well-known authentication methods in accessing many Internet of Things (IoT) devices. The usage of passwords, however, inherits several drawbacks and emerging vulnerabilities in the IoT platform. However, many solutions have been proposed to tackle these limitations. Most of these defense strategies suffer from a lack of computational power and memory capacity and do not have immediate cover in the IoT platform. Motivated by this consideration, the goal of this paper is fivefold. First, we analyze the feasibility of implementing a honeyword-based defense strategy to prevent the latest developed server-side threat on the IoT domain’s password. Second, we perform thorough cryptanalysis of a recently developed honeyword-based method to evaluate its advancement in preventing the threat and explore the best possible way to incorporate it in the IoT platform. Third, we verify that we can add a honeyword-based solution to the IoT infrastructure by ensuring specific guidelines. Forth, we propose a generic attack model, namely matching attack utilizing the compromised password-file to perform the security check of any legacy-UI approach for meeting the all essential flatness security criterion. Last, we compare the matching attack’s performance with the corresponding one of a benchmark technological methods over the legacy-UI model and confirm that our attack has 5%~22% more vulnerable than others.

Accumulated studies have discovered that circular RNAs (CircRNAs) are closely related to many complex human diseases. Due to this close relationship, CircRNAs can be used as good biomarkers for disease diagnosis and therapeutic targets for treatments. However, the number of experimentally verified circRNA-disease associations are still fewer and also conducting wet-lab experiments are constrained by the small scale and cost of time and labour. Therefore, effective computational methods are required to predict associations between circRNAs and diseases which will be promising candidates for small scale biological and clinical experiments. In this paper, we propose novel computational models based on Graph Convolution Networks (GCN) for the potential circRNA-disease association prediction. Currently most of the existing prediction methods use shallow learning algorithms. Instead, the proposed models combine the strengths of deep learning and graphs for the computation. First, they integrate multi-source similarity information into the association network. Next, models predict potential associations using graph convolution which explore this important relational knowledge of that network structure. Two circRNA-disease association prediction models, GCN based Node Classification (GCN-NC) and GCN based Link Prediction (GCN-LP) are introduced in this work and they demonstrate promising results in various experiments and outperforms other existing methods. Further, a case study proves that some of the predicted results of the novel computational models were confirmed by published literature and all top results could be verified using gene-gene interaction networks.

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.

In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which makes tracing problematic vaccine hard and identifying the responsibility for a vaccine accident hard. To solve these issues, this article presents a blockchain-based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain-based tracing system to implement this model. The proposed system takes the blockchain as a global, unique, and verifiable database to store all the circulation data. Through data insertions and queries on the global and unique database, the proposed system achieves the protection of vaccine circulation. We also implement a proof-of-concept prototype of the proposed system. Experimental results confirm that the proposed system is beneficial.

In recent years, multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications. Despite the significant advances in multi-view clustering, two challenges still need to be addressed, i.e., how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering. In this study, we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering (TW-Co-MFC) approach to address the aforementioned issues. In TW-Co-MFC, a two-level weighting strategy is devised to measure the importance of views and features, and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency. Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering. Experiments on real-world datasets show the effectiveness of the proposed approach.

Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that recommends most likely diseases to drugs based on drug and disease related information. Therefore, many recommendation methods can be adopted to drug repositioning. Collaborative metric learning (CML) algorithm can produce distance metrics that capture the important relationships among objects, and has been widely used in recommendation domains. By applying CML in drug repositioning, a joint metric space is learned to encode drug's relationships with different diseases. In this study, we propose a novel drug repositioning computational method using Collaborative Metric Learning to predict novel drug-disease associations based on known drug and disease related information. Specifically, the proposed method learns latent vectors of drugs and diseases by applying metric learning, and then predicts the association probability of one drug-disease pair based on the learned vectors. The comprehensive experimental results show that CMLDR outperforms the other state-of-the-art drug repositioning algorithms in terms of precision, recall, and AUPR.

In the era of the Internet of Things (IoT), people access many applications through smartphones for controlling smart devices. Therefore, such a centralized node must follow a robust access control mechanism so that an intruder cannot control the connected devices. Recent reports suggest that password can be used as an authentication factor for accessing the smart setups. However, this static information can be compromised under the light of different machine learning (ML)-empowered attack mechanisms. Alarmingly, different sensors used in the IoT setup can also expose this static information to the adversaries. Password-based authentication that uses a challenge–response strategy is an effective solution for handling such threat scenarios. In this article, at first, we show that no existing usable challenge–response protocol is safe to be used in the public area network. Following this, we propose a challenge–response protocol that is more secure to use in the public domain. By using eight classifiers, we show that a learning-based threat specific to our protocol has a marginal impact on the method’s security standard. The discussion in this article also suggests that the proposed protocol has usability and security advantages compared to the existing state of the art (e.g., reduces the number of interactions between the user and verifier by a factor of 0.5).

Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD MVMF. These evaluation metrics verify that PCD MVMF has better prediction performance than other methods. To further illustrate the performance of PCD MVMF, case studies of common diseases are conducted. Therefore, PCD MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.

Different biomedical computing methods for cancer-specific gene recognition have been developed in recent years. Currently, building an open-box machine learning system to discover explainable knowledge from gene expression data is a difficult research problem due to a large number of genes, a small number of samples, and noise. Fuzzy systems can be used to deal with data ambiguity and noise issues and extract meaningful knowledge from gene data. In this article, we create a new deep fuzzy neural network to handle the uncertainty in gene data to generate useful knowledge for specific disease diagnosis. A new hybrid algorithm is designed to preprocess data and select informative genes for accurate cancer detection. Various experiments using six different cancer datasets indicate that the new method has better and more reliable performance than the other conventional classification methods with different gene selection methods.

Cloud computing offers a cost-effective and elastic computing paradigm that facilitates large scale data storage and analytics. By deploying virtualization technologies in the datacenter, cloud enables efficient resource management and isolation for various big data applications. Since the hotspots (i.e., overloaded machines) can degrade the performance of these applications, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). However, the previous load balancing schemes make migration decisions based on deterministic resource demand estimation and workload characterization, without considering their stochastic properties. By studying real world traces, we show that the resource demand and workload of virtual machines are highly dynamic and bursty, which can cause these schemes to make inefficient migrations for load balancing. To address this problem, in this paper we propose a stochastic load balancing scheme which aims to provide probabilistic guarantee against the resource overloading with virtual machine migration, while minimizing the total migration overhead. Our scheme effectively addresses the prediction of the distribution of resource demand and the multidimensional resource requirements with stochastic characterization. Moreover, as opposed to the previous works that measure the migration cost without considering the network topology, our scheme explicitly takes into account the distance between the source physical machine and the destination physical machine for a virtual machine migration. The trace-driven experiments show that our scheme outperforms the previous schemes in terms of SLA violation and the migration cost.

Due to its wide range of applications, distributed beamforming (DB) has become one of the most popular study subjects in recent years. The critical problem of detecting distributed beamforming in achieving the carrier phase alignment at the receiver end. This paper presents an iterative algorithm that uses trial and error (TE) learning to attain phase alignment between the sending nodes that only requires 1-bit feedback from the receiver. This algorithm fits our case best, as the convergence speed is gained by obtaining the feedback information more competently than current solutions and improving the received signal strength (RSS) on the receiving end.

Cloud computing is recently getting increasingly popular for supporting scientific applications and complex business processes. Clouds are highly potent for executing workflow-based tasks due to the fact that they provide elastic resource provisioning styles through which computational-intensive workflows can obtain requested resources according to their elastic demand and establish execution environment over virtual machines (VMs). However, it remains a challenge to guarantee cost-effectiveness and quality of service of workflow deployed upon clouds due to the fact that real-world cloud infrastructures are usually with fluctuating and time-varying performance. Existing researches mainly consider that cloud infrastructures are with fixed, random, or bounded quality of service (QoS). In this work, however, we consider that scientific computing processes to be supported by decentralized cloud infrastructures with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-variation-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) VM Selection strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we perform extensive experimental case analysis over real-world third-party commercial clouds and show that our method clearly beats existing approaches.

Four corpora are created to investigate the self-mentions, keywords and sentiment of abstracts. First, self-mentions are categorized to examine the authorial interactions with the reader. Then, the study of high-frequency words and keywords is conducted with different Python programs and the software AntConc. The keywords generated with WordCloud and TF-IDF-LDA methods show a definite relation with high-frequency words generated by Jieba_Counter and NLTK FreqDist. Further, the sentiment analysis is performed with SnowNLP and TextBlob yielding different results, which verifies the authorial interactions with the reader and increased factual information respectively. Finally, the verification by reference corpora validates the consistency of the sentiment analysis by these two methods. The research suggests that the methods for high-frequency words generation, keywords generation and sentiment analysis be selected discriminatively since different methods generate different results; meanwhile, the study verifies that the objectivity remains in the writing of abstracts. The investigation is conducive to the choices of keywords generation and self-mentions in writing.

As IoT devices are always connected to mobile devices or other computing devices via the Internet, clever malwares targeting IoT devices or other computing devices connected to IoT devices are emerging. Therefore, effective IoT security research is needed to respond to hacking attacks by these kinds of malware. This paper studied the method of identifying and analyzing malware combined with social engineering from the perspective of digital forensics. The paper classified and analyzed intelligent malware characteristics and proposed a method of quickly identifying and analyzing the malware that secretly intruded into the devices installed with Android, Linux OS, using digital forensics techniques. Moreover, this paper proved its effectiveness by applying this investigation method to two actual malware cases. The research outcomes will be useful in responding to increasingly clever malware attacking IoT devices.

Nowadays, 5G network is considered to be one of the main pillars of various industries, including the Internet of Things (IoT), smart cities, virtual reality, and many more. Unlike previous network generations, 5G utilizes complex digital technologies such as massive Multiple Input Multiple Output (mMIMO) and runs over higher radio frequencies. The introduction of new technologies and advanced features in the 5G network raises new challenges for network operators, and merging Artificial Intelligence (AI) is one of the effective solutions to address these complexities. However, AI-enabled 5G network engenders security concerns and requires improvement to meet the standardization and qualification of the new network generation. To mitigate these dilemmas, Blockchain must be integrated. Blockchain, as a decentralized methodology provides a secure sharing of information and resources among various nodes of 5G environments. Blockchain can support other technologies, such as AI-based 5G, to create smarter, more efficient, and secure cellular networks. In this article, we present a comprehensive intelligence and secure data analytics framework for 5G networks based on the convergence of Blockchain and AI named “Block5GIntell”. We depict the applications of Blockchain and AI on 5G networks separately and we argue on the support that Blockchain can provide for AI to create smart and secure 5G networks relying on our proposed framework. To support our proposition, we present an energy-saving case study using Blockchain for AI-enabled 5G. The simulation shows an overall 20% decrease in energy consumption at the RAN level.

– In the recent year, with the emergence of various smart devices, the data is explosively increasing in the social Internet of Things (IoT) such as human healthcare. These data mainly involve information about user behaviors collected from various heterogeneous wireless sensor and social networks. Therefore, it is vital to analyze the data to find hidden meaning and convert it into valuable information. Due to the lack of capability to handle a wide range of queries, a traditional relational database provides inefficient analysis for the data. A graph database can easily store and analyze the data from various heterogeneous wireless sensor and social network using team formation algorithm. In the healthcare field, it is important to form a team that manages patients' health efficiently. The final goal of team formation is to organize experts who can perform task of data analysis. However, the existing team formation algorithms rely on a centralized computing environment and require high communication cost among experts to form a team. In this paper, we propose a parallel team formation method on apache spark (PTFS) to analyze graph data considering the crowd intelligence capability that exists in the graph data and social network. The PTFS employs two computation stages - a find skill and a merger subgraph and provides the parallel execution of many map tasks of graph data analysis. The experimental evaluation of the proposed method on a graph dataset demonstrates that it minimizes the communicating cost of the team members to form an optimized expert team in which a desired skill set is assigned to accomplish the graph data analysis.

Background Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

Virtual machine (VM) packing plays an important role in improving resource utilization in cloud data centers. Recently, memory content similarity among VM instances has been used to speed up multiple VM migration in large clouds. Based on this, many VM packing algorithms have been proposed, which only considered the memory capacity of physical machines (PMs) as the resource constraint. However, in practice the results of such algorithms are not feasible, because thy may not satisfy the constraints of multiple resources (e.g., CPU of the PMs). Besides, the granularities of memory sharing in existing studies are very coarse, and they cannot fully leverage the benefits of memory content similarity which mainly appears at memory page level. In this paper, we study the page-sharing-based VM packing that considers constraints in multiple resources. Given a set of VM instances that share a large number of common memory pages, we pack them into the minimum number of PMs, subject to the constraints in the multiple resources on the PMs. This problem is solved in two steps. First, we pack the maximum number of VMs into a given PM, and then propose an approximation algorithm. The approximation ratio is better than that of the existing algorithm. Then, based on this approximation algorithm, we propose a heuristic algorithm to solve the general problem. Experimental results show that our heuristic algorithm outperforms existing approaches with at most 25% less required PMs and at most 40% less memory page transferring.

Hybrid applications (apps) are becoming more and more popular due to their cross-platform capabilities and high performance. These apps use the JavaScript (JS) bridge communication scheme to interoperate between native code and Web code. Although greatly extending the functionalities of hybrid apps by enabling cross-language invocations and making them more powerful, the bridge communication scheme might also cause some new security issues, e.g., cross-language code injection attacks and privacy leaks. In this paper, we propose BridgeTaint, a bi-directional dynamic taint tracking method that can detect bridge security issues in hybrid apps. BridgeTaint uses a method different from existing ones to track tainted data: it records the taint information of sensitive data when the data are transmitted through the bridge, and uses a cross-language taint mapping method to restore the taint tags of corresponding data. Such a novel design enables BridgeTaint to dynamically track tainted data during the execution of the app and analyze hybrid apps developed using frameworks, which cannot be done with existing solutions based on static code analyses. Based on BridgeTaint, we implement the BridgeInspector tool to detect cross-language privacy leaks and code injection attacks in hybrid apps using JS bridges. A benchmark called BridgeBench is also developed for bridge communication security test. The experimental results on BridgeBench and 1172 apps from Android market demonstrate that BridgeInspector can effectively detect potential privacy leaks and cross-language code injection attacks in hybrid apps using bridge communications.

In this chapter, the application of a DLRNN is demonstrated to simulate rainfall-runoff processes and recognize the transition of UHs in hydrology. Although most neural networks are black-box models that lack physical meanings of weights, the DLRNN developed in this chapter connects its weights with UHs that reveal the physical concepts from the network based on the special structure of RNNs. Without trial and error method, the structure and the weights of DLRNN can be quickly determined through a modified form of system identification that combines indirect system identification with the subspace algorithm. Then, the DLRNN learning algorithm based on the interchange of the roles of the network state variables and the weight matrix is derived for on-line training. In this chapter, the DLRNN introduced can not only simulate rainfall-runoff processes, but also recognize the transition of UHs. Owing to the feedback connections, DLRNN performs rainfall-runoff simulations as dynamic systems, and the advantage of DLRNN's dynamic feature has been proven after the comparison between DLRNN and FNN. The investigation of the connections between weights and physical meanings is an extension of neural networks applied in hydrological field due to the linearization of the RNN. Based on the linearization, weights of DLRNN are treated as Markov parameters to realize the transition of UHs. Through on-line learning, DLRNN modifies the weights to capture the relation between rainfall and runoff every time step, and the transition of rainfall-runoff processes can be emerged based on the changes of UHs. Furthermore, a modified system identification that combines indirect system identification with subspace algorithm is described to calibrate the DLRNN. This method determines the quantity of neurons in hidden layer and the weights of the network. It overcomes the drawback of costing time by traditional trial and error search for optimum structure of DLRNN. Additionally, the different forms of DLRNN have also been discussed herein. The results show that the performances of DLRNNs in different forms are close. Hence, the transformation of canonical form can be ignored in the flowchart of simulation via DLRNN. Finally, four criteria have been applied to evaluate the performance of rainfall-runoff simulation via DLRNN. The results show that the performance is satisfactory and DLRNN is competent to simulate dynamic systems, like rainfall-runoff processes.

Regression testing is an essential and critical part of smart terminal program development. The test case suite is usually preprocessed by test case prioritization technology to improve the efficiency of regression testing. To address the problems of traditional genetic algorithm in solving the test case prioritization problem, this paper proposed a test case prioritization algorithm for intelligent terminal based on artificial immune algorithm. Firstly, different sequences of test case sets were used as the encoding of antibodies to initialize the antibody population; secondly, the Hemming distance was introduced as the concentration index of antibodies to calculate the incentive degree; finally, the antibodies were immunized to find the optimal test case set sequence. The experimental results showed that the algorithm based on the artificial immune algorithm was more capable of global search and less likely to fall into local optimum than the genetic algorithm, which indicated that the artificial immune algorithm was more stable and could better solve the test case prioritization problem.

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomenon like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task integrated with live APIs and show that the dialogue simulator is an essential component of the system that leads to over 50% improvement in turn-level action signature prediction accuracy.

The growth of cyber-physical systems (CPS) has greatly improved industrial efficiency and consumer convenience. Nevertheless, the widespread deployment of 5G-CPS systems also poses new problems that are of major concern to security. In order to help create stable and robust 5G-CPS systems, systematic analyzes are carried out on CPS and IoT from individual smart devices that act as terminal nodes to the entire network. The results of the review will help to find weaknesses in CPS systems and/or provide guidance on how to create protection in these systems using deep learning and blockchain techniques. Risk reduction approaches that can help improve protection will also be addressed.

Microbes play a crucial role in human health and disease. Understanding the relationship between microbes and diseases is conducive to the treatment and diagnosis of diseases. Recently, many computational methods have been proposed to predict disease-microbe associations. However, most of the existing methods only consider a single model and explore the disease-microbe associations from a single view. To improve the prediction accuracy, we propose a novel multi-view approach that fuses the linear and nonlinear features to predict new potential associations between diseases and microbes. We first design a non-negative matrix tri-factorization method to extract the linear features of diseases and microbes. We input the linear features from the non-negative matrix tri-factorization model and bi-random walk model into a capsule neural network to obtain the diseases and microbes’ nonlinear features. Finally, we leverage a logistic regression model to combine the non-negative matrix tri-factorization model predictions, bi-random walk model predictions and the capsule neural network predictions to obtain the final association scores between microbes and diseases. We apply our method to predict human microbedisease associations. Experimental results show that our fusion model outperforms the non-negative matrix tri-factorization model, bi-random walk model and other existing models.

With accumulating dysregulated circular RNAs (circRNAs) in pathological processes, the regulatory functions of circRNAs, especially circRNAs as microRNA (miRNA) sponges and their interactions with RNA-binding proteins (RBPs), have been widely validated. However, the collected information on experimentally validated circRNA-disease associations is only preliminary. Therefore, an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs and diseases in diverse species is necessary. Here, we present an updated CircR2Disease v2.0 with the increased number of circRNA-disease associations and novel characteristics. CircR2Disease v2.0 provides more than 5-fold experimentally validated circRNA-disease associations compared to its previous version. This version includes 4201 entries between 3077 circRNAs and 312 disease subtypes. Secondly, the information of circRNA-miRNA, circRNA-miRNA-target, and circRNA-RBP interactions has been manually collected for various diseases. Thirdly, the gene symbols of circRNAs and disease name IDs can be linked with various nomenclature databases. Detailed descriptions such as samples and journals have also been integrated into the updated version. Thus, CircR2Disease v2.0 can serve as a platform for users to systematically investigate the roles of dysregulated circRNAs in various diseases and further explore the posttranscriptional regulatory function in diseases. Finally, we propose a computational method named circDis based on the graph convolutional network (GCN) and gradient boosting decision tree (GBDT) to illustrate the applications of the CircR2Disease v2.0 database. CircR2Disease v2.0 is available at http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0 and https://github.com/bioinforlab/CircR2Disease-v2.0.

BackgroundIn the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different‘omics’resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency.ResultsIn this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm calledCMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets ofMultiple tissuesandYeastfrom two different organisms (Homo SapiensandSaccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification.ConclusionsThe experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case ofMultiple Tissuesdata set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. ForYeastdata set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates. We perform experiments on VGG-19 and resnet (Resnet-18 and Resnet-34) models, and study the impact of amplification parameters on these models in detail. Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time., 9 page document with 7 figures and one results table

ICD-9 codes have been widely used to describe a patient's diagnosis. Accurate automatic ICD-9 coding is important because manual coding is expensive, time-consuming. Inspired by the recent successes of deep transfer learning, in this study, we propose a deep transfer learning framework for automatic ICD-9 coding. Our proposed method makes use of transferring MeSH domain knowledge to improve automatic ICD-9 coding. We demonstrate its effectiveness by achieving state-of-the-art performance with a value of 0.420 for Micro-average F-measure on MIMIC-III dataset, which indicates that our method outperforms hierarchy-based SVM and flat-SVM. Furthermore, we analyze the deep neural network structure to discover the vital elements in the success of our proposed method. Our experimental results indicate that transfer learning is the key component to improve the performance of automatic ICD-9 coding and deep learning approach is the foundation in the success of our proposed model. In addition, to explore the best network architecture, we also compare the performance of multi-scale and sequential network architectures and find that using multi-scale network is better. Finally, we investigate the effects of transferring different percentage of samples on transfer learning and the results show that the best performance of target domain task can be obtained when 100% number samples are transferred.

In the domain of password security, research has made significant progress in handling different kinds of threats which require human intelligence factor to fix the vulnerabilities. In spite of having strong theoretical establishments, most of these defense mechanisms cannot be used in practice as humans have limitations in processing complex information. The little bit of good news is that very few research proposals in this field have shown the promises to be deployable in practice. This paper focuses on such one method - proposed by Roth et al. back in 2004, which provides adequate user-friendliness to enter Personal Identification Number (PIN) securely in the presence of human shoulder surfers. Surprisingly, the background algorithm of this method for validating users’ responses runs in linear time on a search space of cardinality 5 and hence, the validation process does not put much load on the authenticating device. Therefore, such human identification protocol can also be integrated into the IoT infrastructure for conducting a more secured login from the client-side. Having such advantages, though remained secure for almost ten years after its release in 2004, recently, few proposals revealed some serious vulnerable aspects of the Roth et al. ’s proposal. In this paper, we have taken an attempt to save this user-friendly form of authentication. Firstly, we have made a critical discussion on the importance of the targeted PIN entry method in the domain of usable security and then given a brief overview of the identified limitations of this protocol. Followed by this, a few initiatives have been taken to fix the identified vulnerabilities of Roth et al. ’s proposal by revising its working principle, while the login procedure and the usability standard of this method stay unaffected.

Purpose Multiple imputation (MI) is a widely acceptable approach to missing data problems in epidemiological studies. Composite variables are often used to summarize information from multiple, correlated items. This study aims to assess and compare different MI methods for handling missing categorical composite variables. Methods We investigate the problem in the context of a real application: estimating the prevalence of HIV transmission category, which is a composite variable generated by applying a hierarchical algorithm to a group of binary risk source variables from a national program data set. We use simulation studies to compare and assess the performance of alternative MI strategies. These methods include the active imputation, just another variable, and the passive imputation approaches. Results Our study suggests that the passive imputation approach performs better than the direct imputation approach and the inclusive and general imputation model (i.e. passive imputation with interactions) performs the best. There is no need to embed the information from the variable-combining algorithm in the passive imputation modeling. Conclusion We recommend practitioners adopting an inclusive and general passive imputation modeling strategy.

An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html.

Motivation Reconstructing gene regulatory networks (GRNs) based on gene expression profiles is still an enormous challenge in systems biology. Random forest-based methods have been proved a kind of efficient methods to evaluate the importance of gene regulations. Nevertheless, the accuracy of traditional methods can be further improved. With time-series gene expression data, exploiting inherent time information and high order time lag are promising strategies to improve the power and accuracy of GRNs inference. Results In this study, we propose a scalable, flexible approach called BiXGBoost to reconstruct GRNs. BiXGBoost is a bidirectional-based method by considering both candidate regulatory genes and target genes for a specific gene. Moreover, BiXGBoost utilizes time information efficiently and integrates XGBoost to evaluate the feature importance. Randomization and regularization are also applied in BiXGBoost to address the over-fitting problem. The results on DREAM4 and Escherichia coli datasets show the good performance of BiXGBoost on different scale of networks. Availability and implementation Our Python implementation of BiXGBoost is available at https://github.com/zrq0123/BiXGBoost. Supplementary information Supplementary data are available at Bioinformatics online.

Several anatomical magnetic resonance imaging (MRI) markers for Alzheimer's disease (AD) have been identified. Cortical gray matter volume, cortical thickness, and subcortical volume have been used successfully to assist the diagnosis of Alzheimer's disease including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Currently, these anatomical MRI measures have mainly been used separately. Thus, the full potential of anatomical MRI scans for AD diagnosis might not yet have been used optimally. Meanwhile, most studies currently only focused on morphological features of regions of interest (ROIs) or interregional features without considering the combination of them. To further improve the diagnosis of AD, we propose a novel approach of extracting ROI features and interregional features based on multiple measures from MRI images to distinguish AD, MCI (including MCIc and MCInc), and health control (HC). First, we construct six individual networks based on six different anatomical measures (i.e., CGMV, CT, CSA, CC, CFI, and SV) and Automated Anatomical Labeling (AAL) atlas for each subject. Then, for each individual network, we extract all node (ROI) features and edge (interregional) features, and denoted as node feature set and edge feature set, respectively. Therefore, we can obtain six node feature sets and six edge feature sets from six different anatomical measures. Next, each feature within a feature set is ranked by $F$ -score in descending order, and the top ranked features of each feature set are applied to MKBoost algorithm to obtain the best classification accuracy. After obtaining the best classification accuracy, we can get the optimal feature subset and the corresponding classifier for each node or edge feature set. Afterwards, to investigate the classification performance with only node features, we proposed a weighted multiple kernel learning (wMKL) framework to combine these six optimal node feature subsets, and obtain a combined classifier to perform AD classification. Similarly, we can obtain the classification performance with only edge features. Finally, we combine both six optimal node feature subsets and six optimal edge feature subsets to further improve the classification performance. Experimental results show that the proposed method outperforms some state-of-the-art methods in AD classification, and demonstrate that different measures contain complementary information.

The Internet of things (IoT) in the cloud has been increasingly utilized in recent years as more and more network data are being processed. It plays an important role in the distributed processing of networks by applying fog computing to process huge volumes of data. Distributed fog computing has the advantage of distributed processing, but a new architecture is required to ensure the high performance and simple management of distributed networks. CF-CloudOrch needs to solve many problems, including security, flexibility, high performance, scalability, and efficiency, which are IoT and distributed fog computing. This paper proposes a new container fog node-based cloud orchestration that meets the requirements. The proposed CF-CloudOrch is a prototype of a network management with a lighter container technology than virtualization. CF-CloudOrch understands flow diagrams through prototype implementation and service scenarios and presents a comparative analysis with other environments. Finally, the IoT network contributes to future IT research through efficient and simple management.

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tissue contrast, in recent years, Magnetic Resonance Imaging (MRI) has been attracting increasing attention. With the development of deep learning, many innovative deep learning methods have been proposed to improve MRI image processing and analysis performance. The purpose of this article is to provide a comprehensive overview of deep learning-based MRI image processing and analysis. First, a brief introduction of deep learning and imaging modalities of MRI images is given. Then, common deep learning architectures are introduced. Next, deep learning applications of MRI images, such as image detection, image registration, image segmentation, and image classification are discussed. Subsequently, the advantages and weaknesses of several common tools are discussed, and several deep learning tools in the applications of MRI images are presented. Finally, an objective assessment of deep learning in MRI applications is presented, and future developments and trends with regard to deep learning for MRI images are addressed.

Distributed link-state routing protocols, including Open Shortest Path First (OSPF) and Intermediate System–Intermediate System (IS-IS), have successfully provided robust shortest path routing for IP networks. However, shortest path routing is inflexible and sometimes results in congestion on some critical links. By separating the control plane and the data plane, the centralized control of Software Defined Networking (SDN)-based approach possesses flexible routing capabilities. Fibbing is an approach that can achieve centralized control over a network running distributed routing protocols. In a Fibbing-controlled IP network, the controller cleverly generates fake protocol messages to manipulate routers to steer the flow of the desired paths. However, introducing fake nodes destroys the structure of the loop-free property of Loop-Free Alternate (LFA) that is used to achieve fast failure recovery in IP networks. This paper addresses this issue and presents a solution to provision routing paths so a Fibbing network can still apply LFA in the network. The proposed network jointly considers load-balanced and fast failure recovery. We formulate the problem as an integer linear programming problem. The numerical results reveal that the proposed method can provide 100% survivability against any single node or single link failure.

Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR entities as individual features, and no relationships between them are taken into consideration. We propose to evaluate the relationships between EHR features and map them into Procedures, Prescriptions, and Diagnoses (PPD) tensor data, which can be formatted as images. The mapped images are then fed into deep convolutional networks for local pattern and feature learning. We add this relationship-learning part as a boosting module on a commonly used classical machine learning model. Experiments were performed on a Chronic Lymphocytic Leukemia dataset for treatment initiation prediction. Experimental results show that the proposed approach has better real world modeling performance than the baseline models in terms of prediction precision.

Modern data centers host diverse hyper text transfer protocol (HTTP)-based services, which employ persistent transmission control protocol (TCP) connections to send HTTP requests and responses. However, the ON/OFF pattern of HTTP traffic disturbs the increase of TCP congestion window, potentially triggering packet loss at the beginning of ON period. Furthermore, the transmission performance becomes worse due to severe congestion in the concurrent transfer of HTTP response. In this paper, we provide the first extensive study to investigate the root cause of performance degradation of highly concurrent HTTP connections in data center network. We further present the design and implementation of TCP-TRIM, which employs probe packets to smooth the aggressive increase of congestion window in persistent TCP connection and leverages congestion detection and control at end-host to limit the growth of switch queue length under highly concurrent TCP connections. The experimental results of at-scale simulations and real implementations demonstrate that TCP-TRIM reduces the completion time of HTTP response by up to 80%, while introducing little deployment overhead only at the end hosts.

Motivation Cheap and fast next generation sequencing (NGS) technologies facilitate research of de novo assembly greatly. The reliability of contigs is critical to construct reliable scaffolding. However, contigs generated from most assemblers contain errors because of the limitation of assembly strategy and computation complexity. Among all these errors, the misassembly error is one of the most harmful types. Results In this paper, we propose a new method named “PECC” to identify and correct misassembly errors in contigs based on the paired-end read distribution. PECC extracts sequence regions with lower paired-end reads supports and verifies them based on the distribution of paired-end supports. To validate the effectiveness of PECC, we applied PECC to the contigs produced by five popular assemblers on four real datasets, and we also carried out experiments to analyze the influences of PECC on scaffolding. The results show that PECC can reduce misassembly errors and improve the performance of scaffolding results, which demonstrate the promising applications of PECC in de novo assembly.

Distributed stream processing systems need to support stateful processing, recover quickly from failures to resume such processing, and reprocess an entire data stream quickly. We present Apache Samza, a distributed system for stateful and fault-tolerant stream processing. Samza utilizes a partitioned local state along with a low-overhead background changelog mechanism, allowing it to scale to massive state sizes (hundreds of TB) per application. Recovery from failures is sped up by re-scheduling based on Host Affinity. In addition to processing infinite streams of events, Samza supports processing a finite dataset as a stream, from either a streaming source (e.g., Kafka), a database snapshot (e.g., Databus), or a file system (e.g. HDFS), without having to change the application code (unlike the popular Lambda-based architectures which necessitate maintenance of separate code bases for batch and stream path processing). Samza is currently in use at LinkedIn by hundreds of production applications with more than 10, 000 containers. Samza is an open-source Apache project adopted by many top-tier companies (e.g., LinkedIn, Uber, Netflix, TripAdvisor, etc.). Our experiments show that Samza: a) handles state efficiently, improving latency and throughput by more than 100X compared to using a remote storage; b) provides recovery time independent of state size; c) scales performance linearly with number of containers; and d) supports reprocessing of the data stream quickly and with minimal interference on real-time traffic.

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unraveling complex relationships between genotypes and phenotypes. Current multi-locus-based methods are insufficient to detect interactions with diverse genetic effects on multifarious diseases. Also, statistic tests for high-order epistasis ( $\geq 2$ SNPs) raise huge computational and analytical challenges because the computation increases exponentially as the growth of the cardinality of SNPs combinations. In this paper, we provide a simple, fast and powerful method, named DAM, using Bayesian inference to detect genome-wide multi-locus epistatic interactions in multiple diseases. Experimental results on simulated data demonstrate that our method is powerful and efficient. We also apply DAM on two GWAS datasets from WTCCC, i.e . , Rheumatoid Arthritis and Type 1 Diabetes, and identify some novel findings. Therefore, we believe that our method is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

We propose an approach of generating a hybrid feature set and using prior knowledge in a multi-stage CNNs for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound. The novel multi-stage CNNs method work together with prior knowledge constraints in decision making to overcome the limited data problem in infant sound classification. Prior knowledge either from rules or from statistical results provides a good guidance for searching and classification. The effectiveness of proposed method is evaluated on commonly used Dustan Baby Language Database and Baby Chillanto Database. It gives an encouraging reduction of 4.14% absolute classification error rate compared with the results from the best model using one-stage CNN. In addition, on Baby Chillanto Database, a significant absolute error reduction of 5.33% is achieved compared to one-stage CNN and it outperforms all other existing related studies.

The cloud computing paradigm is characterized by the ability to provide flexible provisioning patterns for computing resources and on-demand common services. As a result, building business processes and workflow-based applications on cloud computing platforms is becoming increasingly popular. However, since real-world cloud services are often affected by real-time performance changes or fluctuations, it is difficult to guarantee the cost-effectiveness and quality-of-service (Qos) of cloud-based workflows at real time. In this work, we consider that workflows, in terms of Directed Acyclic Graphs (DAGs), to be supported by decentralized cloud infrastructures are with time-varying performance and aim at reducing the monetary cost of workflows with the completion-time constraint to be satisfied. We tackle the performance-fluctuation workflow scheduling problem by incorporating a stochastic-performance-distribution-based framework for estimation and optimization of workflow critical paths. The proposed method dynamically generates the workflow scheduling plan according to the accumulated stochastic distributions of tasks. In order to prove the effectiveness of our proposed method, we conducted a large number of experimental case studies on real third-party commercial clouds and showed that our method was significantly better than the existing method.

Online tutoring has gained popularity in recent years, which allows students to get tutoring service from tutors in an online, virtual environment. To support effective online tutoring, an integrated online tutoring system is essential. This paper presents a cloud-based virtual tutoring center that supports online tutoring for college students to complement the tutoring service of physical tutoring centers on a campus. We present the overall architecture of the virtual tutoring center system and show preliminary results of using the virtual tutoring center to provide online tutoring for computer science students.

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.

Background Chromatin immunoprecipitation sequencing (ChIP-seq) is a technology that combines chromatin immunoprecipitation (ChIP) with next generation of sequencing technology (NGS) to analyze protein interactions with DNA. At present, most ChIP-seq analysis tools adopt the command line, which lacks user-friendly interfaces. Although some web services with graphical interfaces have been developed for ChIP-seq analysis, these sites cannot provide a comprehensive analysis of ChIP-seq from raw data to downstream analysis. Results In this study, we develop a web service for the whole process of ChIP-Seq Analysis (CSA), which covers mapping, quality control, peak calling, and downstream analysis. In addition, CSA provides a customization function for users to define their own workflows. And the visualization of mapping, peak calling, motif finding, and pathway analysis results are also provided in CSA. For the different types of ChIP-seq datasets, CSA can provide the corresponding tool to perform the analysis. Moreover, CSA can detect differences in ChIP signals between ChIP samples and controls to identify absolute binding sites. Conclusions The two case studies demonstrate the effectiveness of CSA, which can complete the whole procedure of ChIP-seq analysis. CSA provides a web interface for users, and implements the visualization of every analysis step. The website of CSA is available at http://CompuBio.csu.edu.cn