Pre-trained language models have demonstrated outstanding performance across various natural language understanding tasks. Exploiting the sophisticated understanding capacities of pre-trained language models for humor detection poses a pivotal challenge in the domain of humor recognition. Current methodologies primarily rely on full-parameter fine-tuning of pre-trained language models, often neglecting implicit emotional expressions and incongruity theories inherent in humorous texts. This limitation hinders the effectiveness of humor recognition models. To address these challenges, we propose PromptHR (Prompt-based Humor Recognition), a multi-task learning framework integrating deep commonsense prompt learning and incongruity theory. Specifically, our approach incorporates commonsense knowledge into pre-trained language models through prompt learning, subsequently identifying semantic incongruity through analysis of disparities and interactions between the set-up and punchline in humorous texts. Experimental results show our model achieves state-of-the-art performance on PQA and HAHA tasks, with relative error rate reductions of 12.97% and 17.01%, respectively. This highlights the effectiveness of our approach in pushing the boundaries of humor recognition.

MOTIVATION: Therapeutic peptide is an important ingredient in the treatment of various diseases and drug discovery. The toxicity of peptides is one of the major challenges in peptide drug therapy. With the abundance of therapeutic peptides generated in the post-genomics era, it is a challenge to promptly identify toxicity peptides using computational methods. Although several efforts have been made, few algorithms are designed to identify whether a query peptide exhibits toxicity. Considering the varied levels of biological activities, the toxicity peptides should be further classified into multi-functional peptides. RESULTS: This study introduces a two-level predictor, ToxPre-2L, developed using the multi-view tensor learning and latent semantic learning framework. The proposed method utilized multi-label learning with feature induced labels to avoid the redundancy of information from each view. Then the multi-view tensor learning was employed to establish the latent semantic information among different views, while low-rank constraint learning was leveraged to exploit the correlation information among multi-labels. Finally, we constructed an updated toxicity peptide benchmark dataset to assess the effectiveness of the proposed method. Experimental results demonstrated that ToxPre-2L achieves a better performance than alternative computational methods in the prediction of toxicity peptides and their multi-functional types. AVAILABILITY AND IMPLEMENTATION: The source code and data of ToxPre-2L can be accessed at http://bliulab.net/ToxPre-2L.

Most Seq2Seq neural model-based medical question summarization (MQS) systems have a severe mismatch between training and inference, i.e., exposure bias. However, this problem remains unexplored in the MQS task. To bridge this research gap and alleviate the problem of exposure bias, we propose a novel re-ranking training framework for MQS called Multi-view Contrastive Learning (MvCL). MvCL simultaneously considers the similarity scores between medical questions and candidate summaries as well as the average similarity scores between candidate summaries and other candidates within the same group, and utilizes contrastive learning to optimize the model’s ranking ability. Additionally, we propose a new multilevel inference approach to adapt to this training strategy. The approach first filters out candidate summaries that are dissimilar to the original medical question, and then selects the summary with the highest average similarity to other candidate summaries from the remaining candidates as the final output. We conducted extensive experiments, and the results demonstrate that our proposed MvCL framework achieves state-of-the-art results on the majority of evaluation metrics across four datasets.1

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapeutic approach with widespread application worldwide. The unique diagnostic methods of TCM often require a comprehensive analysis of patient information, much of which is contained in clinical text. Numerous studies have demonstrated the effectiveness of natural language processing (NLP) techniques in TCM disease classification. Therefore, this paper focuses on the task of TCM syndrome differentiation, proposing a novel matching score calculation method and a new label attention calculation method to assist the model in focusing on the relationship between TCM syndrome and disease symptom. Specifically, we enhance the model’s attention to the uniqueness of the relationship between TCM syndrome and symptom by introducing a finer-grained token-level matching score. Simultaneously, we improve the model’s attention to the generality of the relationship between TCM syndrome and symptom through a more global label attention mechanism. Additionally, we observe a severe long-tail problem in the dataset. To alleviate this issue, we propose the use of focal loss to help the model pay more attention to challenging samples. Extensive experiments on the TCM-SD dataset indicate that our approach significantly outperforms state-of-the-art baselines1.

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.

Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http://bliulab.net/GraLTR-LDA.

Nucleic acid-binding proteins are proteins that interact with DNA and RNA to regulate gene expression and transcriptional control. The pathogenesis of many human diseases is related to abnormal gene expression. Therefore, recognizing nucleic acid-binding proteins accurately and efficiently has important implications for disease research. To address this question, some scientists have proposed the method of using sequence information to identify nucleic acid-binding proteins. However, different types of nucleic acid-binding proteins have different subfunctions, and these methods ignore their internal differences, so the performance of the predictor can be further improved. In this study, we proposed a new method, called iDRPro-SC, to predict the type of nucleic acid-binding proteins based on the sequence information. iDRPro-SC considers the internal differences of nucleic acid-binding proteins and combines their subfunctions to build a complete dataset. Additionally, we used an ensemble learning to characterize and predict nucleic acid-binding proteins. The results of the test dataset showed that iDRPro-SC achieved the best prediction performance and was superior to the other existing nucleic acid-binding protein prediction methods. We have established a web server that can be accessed online: http://bliulab.net/iDRPro-SC.

MOTIVATION: Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides. Moreover, it is still challenging to utilize different properties to predict the therapeutic peptides. RESULTS: In this study, an adaptive multi-view based on the tensor learning framework TPpred-ATMV is proposed for predicting different types of therapeutic peptides. TPpred-ATMV constructs the class and probability information based on various sequence features. We constructed the latent subspace among the multi-view features and constructed an auto-weighted multi-view tensor learning model to utilize the high correlation based on the multi-view features. Experimental results showed that the TPpred-ATMV is better than or highly comparable with the other state-of-the-art methods for predicting eight types of therapeutic peptides. AVAILABILITY AND IMPLEMENTATION: The code of TPpred-ATMV is accessed at: https://github.com/cokeyk/TPpred-ATMV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs), ensuring smooth interactions and improving the driving experience. Listening to news is a popular method of relaxing during a journey; as a result, travelers require automatic recommendations of preferred news programs. Most existing news recommender systems usually learn topic-level representations of users and news for recommendations while neglecting to learn more informative aspect-level features, resulting in limited recommendation performance. To bridge this significant gap, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preferences and news representation learning. In ANRS, a news aspect-level encoder and a user aspect-level encoder are devised to learn the fine-grained aspect-level representations of users’ preferences and news characteristics respectively. These representations are subsequently fed into a click predictor to predict the probability of a given user clicking on the candidate news item. Extensive experiments demonstrate the superiority of our method over state-of-the-art baseline methods.

Medical concept embedding which aims at learning interpretable low-dimensional representations of medical codes has become one of the key technologies to enable the machine (deep) learning models to imitate the doctor’s cognitive reasoning process in a variety of clinical tasks. Most existing works focus on leveraging the medical ontology to get the representations but remains ineffective in dealing with 1) the inconsistency between the knowledge of the medical ontology and the observations in health records, and 2) the deficiency of discovering the relations among multi-types of medical concepts. To address these challenges, this paper proposes MrER(Multi-relational EHR representation learning method). It’s a heterogeneous graph convolutional network with a self-adaptive adjacency matrix, to infer the multi-relations among different types of medical concepts and align them in the same subspace for the complex knowledge inference. Moreover, an temporal convolutional network is introduced to capture the dependency patterns in the sequence of medical records. The entire framework is trained in an end-to-end fashion. The experimental results show that MrER achieves competitive performance advantages in sequential diagnosis prediction task in comparison with state-of-the-art methods and the learned embeddings have good interpretability regarding the relationship between medical codes.

Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential dependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.