BACKGROUND: The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS: In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS: The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION: The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one critical issue with this emerging technique is the lack of integration with non-real-time collaboration. Specifically, contributions from multiple programmers in a real-time collaboration session cannot be distinguished and accurately recorded in the version control system. In this study, we propose a scheme that integrates real-time and non-real-time collaborative programming with a novel workflow, and contribute enabling techniques to realize such integration. As a proof-of-concept, we have successfully implemented two prototype systems named CoEclipse and CoIDEA, which allow programmers to closely collaborate in a real-time fashion while preserving the work's compatibility with traditional non-real-time collaboration. User evaluation and performance experiments have confirmed the feasibility of the approach and techniques, demonstrated the good system performance, and presented the satisfactory usability of the prototypes.

Real-time collaborative programming has attracted increasing attention and interest in recent years. To resolve the semantic conflict problem in real-time collaborative programming, a Dependency-based Automatic Locking (DAL) scheme was proposed in prior work. The DAL scheme prevents other collaborators from editing semantically related regions by automatically detecting depended regions and locking them. However, the DAL scheme lacks flexibility and is not well suited to the needs of programmers in a real-world development scenario. When the programmer switches to a new region, all previous locks are automatically released. For this reason, we propose the Multiple Locking Group (MLG) scheme, where each programmer can hold multiple locking groups and switch freely between multiple working regions. Accordingly, three release modes for releasing locking groups are proposed. Each programmer can customize the release modes in a fine-grained manner. In supporting the scheme, we have devised techniques and solutions, implemented a prototype system and conducted a preliminary user evaluation to validate the feasibility, effectiveness and usability of the MLG scheme.

Real-time collaborative programming environments support a team of programmers to edit shared source code at the same time, where each local editing operation is captured and immediately transmitted to remote sites in a fine-grained manner. However, under real-world network conditions, collaborators are usually plagued by data congestion and transmitted errors. In this study, we define and analyze the content relationships among operations, and propose a Context-based Operation Merging Algorithm (COMA). Technically, the COMA examines the content relationships among a series of editing operations and merges content-related operations. Powered by the COMA, real-time collaborative programming environments can significantly compress editing operations to cope with complex network situations (such as network fluctuation and interruption) and improve the user experience of real-time collaboration. The proposed COMA has been implemented in a real-time collaborative programming environment prototype, namely CoEclipse. Preliminary user evaluations, correctness analysis and performance evaluations have demonstrated the effectiveness, correctness, and efficiency of the algorithm.

Malocclusion has a high prevalence in the population, which seriously affects patients’ oral and mental health. Angle’s classification is a widely accepted diagnostic standard for malocclusion, either requiring professional intervention and complicated procedures, or increasing radiation risks. This paper proposes a new method of Angle’s classification based on occlusal contact information to realize the automatic Angle’s classification. Firstly, a novel bite force measurement device is used to record the occlusal data of subjects with different occlusal categories, Meta-analysis evaluated several occlusion quantitative evaluation indicators. Then, the imbalance of the data set is improved by oversampling and popular machine learning models are used for training and performance evaluation. The result shows that the accuracy of the random forest model combined with occlusal contact information reaches 87.83%, and the performance of other evaluation indexes is good. It is demonstrated that machine learning models can be applied to Angle’s classification and shows the great potential of occlusal contact information in the aided diagnosis of oral diseases.

Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data. Such approaches are impractical since labeled data are usually hard to obtain in reality. In this paper, we build a clustering feature decision tree model, CFDT, from data streams having both unlabeled and a small number of labeled examples. CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while generating high classification accuracy with high speed.

This paper presents a new test access mechanism (TAM) architecture and optimization method based on an improved flexible-width test bus. The method is first to set up the test time lower bound that is not depends on TAM architecture, then to construct a bus assignment that makes test time up to the lower bound. We present experimental results on our improved flexible-width test buses for four benchmark SOCs. Experiment results in a significant reduction of the test time, and is better than the proposed traditional methods in test time.