MOTIVATION: Intrinsic disorder regions (IDRs) play a significant role in diverse biological processes and are widely distributed in proteins. Thus, accurately predicting these regions is essential for analyzing protein structure and function. Amino acid feature extraction servers as a foundational process in the development of computational predictive models. Existing methods typically rely on traditional biological features (e.g. PSSM) or use pre-trained protein language models (PPLMs) to capture sequence semantic information, often resorting to straightforward feature concatenation. However, these approaches fail to capture the multi-semantic interactions between traditional biological features and PPLMs-based features. RESULTS: In this study, we propose a method named FusionEncoder designed for the integration of traditional biological and PPLMs-based features of the protein. FusionEncoder is a fusion network built on a variant of long short-term memory (LSTM). We consider traditional biological features and PPLMs-based features to be two types of semantic inputs within a "multi-semantic" space. Traditional features are input into the cell state of the LSTM, while PPLMs-based features are fed into the input part. A fusion cell is then utilized to fuse these two types of features. This strategy leverages the capability of LSTM to encode long sequences, enhancing context-aware semantic learning of amino acid sequences. Finally, a transformer-based encoder layer is employed to predict the IDRs. Evaluation on four independent test datasets indicate that FusionEncoder obviously improves the accuracy of amino acid feature representation and achieves superior performance compared to the other existing methods. AVAILABILITY AND IMPLEMENTATION: To facilitate accessibility for experimental researchers, a user-friendly and publicly available webserver for the FusionEncoder predictor has been deployed at http://bliulab.net/FusionEncoder/. FusionEncoder is expected to serve as a valuable tool for the accurate identification of IDRs.

Medication recommendation is a fundamental yet crucial branch of healthcare that presents opportunities to assist physicians in making more accurate medication prescriptions for patients with complex health conditions. Previous studies have primarily concentrated on deriving patient representations from electronic health records (EHRs) to recommend medications, often overlooking the effective integration of domain-specific prior knowledge. However, integrating domain knowledge with the patient’s clinical manifestations can be challenging, particularly when dealing with complex clinical manifestations. Therefore, in this paper, we first identify comprehensive domain-specific prior knowledge, namely the Unified Medical Language System (UMLS), which is a comprehensive repository of biomedical vocabularies and standards, for knowledge extraction. Subsequently, we propose a knowledge injection module that addresses the effective integration of domain knowledge with complex clinical manifestations, enabling an effective characterization of the health conditions of the patient. Moreover, acknowledging the influence of historical medications on patients’ current treatments, we propose a historical medication-aware patient representation module to capture the longitudinal influence of historical medication information on the representation of current patients. Extensive experiments on three publicly benchmark datasets verify the superiority of our proposed method, which outperformed other methods by a significant margin. The code is available at: https://github.com/sherry6247/DKINet.

As production code evolves, test code can quickly become outdated. When test code is outdated, it may fail to capture errors in the programs under test and can lead to serious software bugs that result in significant losses for both developers and users. To ensure high software quality, it is crucial to promptly update the test code after making changes to the production code. This practice ensures that the test code and production code evolve together, reducing the likelihood of errors and ensuring the software remains reliable. However, maintaining test code can be challenging and time-consuming. To automate the identification of outdated test code, recent research has proposed Sitar, a machine learning-based method. Despite Sitar’s usefulness, it has major limitations, including its coarse prediction granularity (at class level), reliance on naming conventions to discover test code, and dependence on manually summarized features to construct machine learning models.

Existing methods for Multimodal Sentiment Analysis (MSA) mainly focus on integrating multimodal data effectively on limited multimodal data. Learning more informative multimodal representation often relies on large-scale labeled datasets, which are difficult and unrealistic to obtain. To learn informative multimodal representation on limited labeled datasets as more as possible, we proposed TMMDA for MSA, a new Token Mixup Multimodal Data Augmentation, which first generates new virtual modalities from the mixed token-level representation of raw modalities, and then enhances the representation of raw modalities by utilizing the representation of the generated virtual modalities. To preserve semantics during virtual modality generation, we propose a novel cross-modal token mixup strategy based on the generative adversarial network. Extensive experiments on two benchmark datasets, i.e., CMU-MOSI and CMU-MOSEI, verify the superiority of our model compared with several state-of-the-art baselines. The code is available at https://github.com/xiaobaicaihhh/TMMDA.

Text, visual, and acoustic are usually complementary in the Multimodal Sentiment Analysis (MSA) task. However, current methods primarily concern shared representations while neglecting the critical private aspects of data within individual modalities. In this work, we propose shared-private memory networks based on the recent advances in the attention mechanism, called SPMN, to decouple multimodal representation from shared and private perspectives. It contains three components: a) a shared memory to learn the shared representations of multimodal data; b) three private memories to learn the private representations of individual modalities, respectively; c) and adaptive fusion gates to fuse multimodal private and shared representations. To evaluate the effectiveness of SPMN, we integrate it into different pre-trained language representation models, such as BERT and XLNET, and conduct experiments on two public datasets, CMU-MOSI and CMU-MOSEI. Experimental results indicate that the performances of pre-trained language representation models are significantly improved because of SPMN and demonstrate the superiority of our model compared to the state-of-the-art methods. SPMN's source code is publicly available at:https://github.com/xiaobaicaihhh/SPMN.

Effectively medication recommendation with complex multimorbidity conditions is a critical yet challenging task in healthcare. Most existing works predicted medications based on longitudinal records, which assumed the encoding format of intra-visit medical events are serialized and information transmitted patterns of learning longitudinal sequence data are stable. However, the following conditions may have been ignored: 1) A more compact encoder for intra-relationship in the intra-visit medical event is urgent; 2) Strategies for learning accurate representations of the variable longitudinal sequences of patients are different. In this article, we proposed a novel Sample-adaptive Hierarchical medicAtion Prediction nEtwork, termed SHAPE, to tackle the above challenges in the medication recommendation task. Specifically, we design a compact intra-visit set encoder to encode the relationship in the medical event for obtaining visit-level representation and then develop an inter-visit longitudinal encoder to learn the patient-level longitudinal representation efficiently. To endow the model with the capability of modeling the variable visit length, we introduce a soft curriculum learning method to assign the difficulty of each sample automatically by the visit length. Extensive experiments on a benchmark dataset verify the superiority of our model compared with several state-of-the-art baselines.

As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increasingly applied to the healthcare domain. Most existing EHR-oriented studies, however, either focus on a particular modality or integrate data from different modalities in a straightforward manner, which usually treats structured and unstructured data as two independent sources of information about patient admission and ignore the intrinsic interactions between them. In fact, the two modalities are documented during the same encounter where structured data inform the documentation of unstructured data and vice versa. In this paper, we proposed a Medical Multimodal Pre-trained Language Model, named MedM-PLM, to learn enhanced EHR representations over structured and unstructured data and explore the interaction of two modalities. In MedM-PLM, two Transformer-based neural network components are firstly adopted to learn representative characteristics from each modality. A cross-modal module is then introduced to model their interactions. We pre-trained MedM-PLM on the MIMIC-III dataset and verified the effectiveness of the model on three downstream clinical tasks, i.e., medication recommendation, 30-day readmission prediction and ICD coding. Extensive experiments demonstrate the power of MedM-PLM compared with state-of-the-art methods. Further analyses and visualizations show the robustness of our model, which could potentially provide more comprehensive interpretations for clinical decision-making.

Recently, speech assistants have brought convenience to our lives from many aspects. In the education field, speech assistants can also help teachers to reduce their burdens. However, there is no suitable solution to synthesize speeches for mathematical formulas although there have been lots of good techniques for text-to-speech (TTS) in the general domain. One possible solution is that we can convert mathematical formulas expressed in the LaTeX format to spelling texts and synthesize them into speech. In this paper, we investigated text generation methods that translate mathematical formulas in LaTex into Chinese spelling texts. For this purpose, we first constructed a parallel corpus of mathematical formulas and Chinese spelling texts, then compared the existing commonly used text generation methods, such as rule-based, Seq2Seq, Transformer and Graph2Seq, and finally proposed a novel model. As far as we know, this is the first study for Chinese spelling text generation of mathematical formulas. Experiment results on the annotated corpus show that our proposed model significantly outperforms the existing commonly used generation models.

Human language is multimodal, including textual, visual and acoustic information. The task of multimodal sentiment analysis is to use human multimodal information for sentiment recognition. Among the three modalities, text contains richer information than other modalities. With the development of pre-trained representation models on text, most of multimodal sentiment analysis methods use text as primary information and the other modalities as supplementary information. The existing methods suffer from the following limitations: 1) inherent heterogeneity of multimodal data, which makes multimodal fusion difficult as different modalities reside in different feature spaces; 2) asynchronism caused by the inconsistent sampling rates of the time series data of different modalities. To alleviate the heterogeneity and asynchronism of multimodal data, we propose HMAI-BERT, a hierarchical multimodal alignment and interaction network-enhanced BERT. In HMAI-BERT, to improve the efficiency of multimodal interaction, we introduce a memory network to align the different multimodal representations before fusion. After multimodal alignment, we propose a modal update method to address the problem of asynchronism, where each modality is reinforced by interacting with other modalities. In addition, we introduce a fusion module to integrate the three reinforced modalities, and a sentiment enhanced memory to enhance multimodal representation. Our experiments on two public datasets show that the proposed HMAI-BERT outperforms the state-of-the-art methods.