Knowledge tracing is a key technology in online education platforms such as Intelligent Tutoring Systems (ITSs) and Massive Open Online Courses (MOOCs), which model the state of an individual’s knowledge based on the learner’s historical sequence of interactions to predict future performance. However, existing knowledge-tracing approaches lack attention to high- and low-order features and exercise relevance, while few models consider the real-world situation of online tutoring systems where learners can only interact with a limited number of exercises and data is often sparse. Therefore, this paper proposes a knowledge tracing method based on the fusion of exercise relevance and hybrid attention network. Firstly, the parallel GRU captures the high and low order features of learners, which are processed by the extraction network to fit the information representation of learner-interacted exercises. Second, the correlation-enhancing features of target exercises and historical interaction exercises are captured. Finally, a prediction network is used to fuse multiple features in order to effectively capture learners’ knowledge states and improve prediction accuracy in the face of sparse data. Extensive experiments on real online education datasets have shown that ERKT achieves better prediction results compared to existing mainstream methods.

BACKGROUND: A Raman spectroscopy method can quickly and accurately measure the concentration of ofloxacin in solution. This method has the advantages of accuracy and rapidity over traditional detection methods. However, the manual analysis methods for the collected Raman spectral data often ignore the nonlinear characteristics of the data and cannot accurately predict the concentration of the target sample. METHODS: To address this drawback, this paper proposes a novel kernel-Huber loss function that combines the Huber loss function with the Gaussian kernel function. This function is used with an improved genetic algorithm-convolutional neural network (GA-CNN) to model and predict the Raman spectral data of different concentrations of ofloxacin in solution. In addition, the paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRU) models to conduct multiple experiments and use root mean square error (RMSE) and residual predictive deviation (RPD) as evaluation metrics. RESULTS: The proposed method achieved an [Formula: see text] of 0.9989 on the test set data and improved by 3% over the traditional CNN. Multiple experiments were also conducted using RNN, LSTM, BiLSTM, and GRU models and evaluated their performance using RMSE, RPD, and other metrics. The results showed that the proposed method consistently outperformed these models. CONCLUSIONS: This paper demonstrates the effectiveness of the proposed method for predicting the concentration of ofloxacin in solution based on Raman spectral data, in addition to discussing the advantages and limitations of the proposed method, and the study proposes a solution to the problem of deep learning methods for Raman spectral concentration prediction.

Efficient exploration of the chemical space to search the candidate drugs that satisfy various constraints is a fundamental task of drug discovery. Advanced deep generative methods attempt to optimize the molecules in the compact latent space instead of the discrete original space, but the mapping between the original and latent spaces is always kept unchanged during the entire optimization process. The unchanged mapping makes those methods challenging to fast adapt to various optimization scenes and leads to the great demand for assessed molecules (samples) to provide optimization direction, which is a considerable expense for drug discovery. To this end, we design a sample-efficient molecular generative method, HelixMO, which explores the scene-sensitive latent space to promote sample efficiency. The scene-sensitive latent space focuses more on modeling the promising molecules by dynamically adjusting the space mapping by leveraging the correlations between the general and scene-specific characteristics during the optimization process. Extensive experiments demonstrate that HelixMO can achieve competitive performance with only a few assessed samples on four molecular optimization scenes. Ablation studies verify the positive impact of the scene-specific latent space, which is capable of identifying the critical characteristics of the promising molecules. We also deployed HelixMO on the website PaddleHelix (https://paddlehelix.baidu.com/app/drug/drugdesign/forecast) to provide drug design service.

It is difficult to predict the elongation of strip steel in annealing furnace due to the influence of temperature, tension, roll speed and data noise. Thus, a strip elongation prediction method based on least squares support vector machine (LSSVM) optimized by artificial bee colony (ABC) algorithm is proposed. In order to improve the convergence speed and accuracy of the algorithm, the new adaptive step update formula, adaptive probability selection formula and global search factor were introduced to improve the standard artificial bee colony algorithm. The parameter of the LSSVM were optimized by improved artificial bee colony(IABC) algorithm which overcame the subjectivity of human selection and made the LSSVM get better generalization and prediction accuracy. Numerical simulation results of MATLAB show that the relative error and root mean square error predicted by IABC-LSSVM are better than those predicted by ABC-LSSVM and LSSVM which effectively improves the convergence speed and prediction accuracy of the algorithm. Under the real working conditions, IABC-LSSVM provides theoretical support for the prediction of strip extension in annealing furnace and has a certain engineering application value.

MOTIVATION: Accurate ADMET (an abbreviation for 'absorption, distribution, metabolism, excretion and toxicity') predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customized to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks and self-supervised tasks. RESULTS: Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customized ADMET endpoints, meeting various demands of drug research and development requirements. AVAILABILITY AND IMPLEMENTATION: H-ADMET is freely accessible at https://paddlehelix.baidu.com/app/drug/admet/train. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. RESULTS: We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. AVAILABILITY AND IMPLEMENTATION: R function and instruction are available from the authors' website: https://www4.stat.ncsu.edu/~jytzeng/Software/TR.omics/TRinstruction.pdf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.