The surge in accessible high-throughput molecular data presents computational challenges for the precision medicine in cancer. Genetic mutations have the potential to act as reliable biomarkers indicating responses to targeted drugs. Accurate prediction of mutation-drug associations is critically important for drug development and cancer treatment. We propose a novel graph convolutional network method, MDAGCN, to predict the mutation-drug associations with specific types (sensitive/resistant) in cancer. To enhance both the efficiency and accuracy of training, we begin by constructing a feature and topological graph using the k-Nearest Neighbors algorithm, incorporating the structural relationship and feature data associated with mutation-drug interactions. Experimental results show that MDAGCN outperforms state-of-the-art methods in different experimental settings. Moreover, we show the effectiveness of graph sampling technique for training signed graphs. MDAGCN is a comprehensive end-to-end framework that could be broadly applicable to cancer pharmacogenomics. This framework has the potential to facilitate the mapping from the discovering novel mutation-drug associations to in-depth analysis of drug sensitivity and resistance.

The intricacies of cancer present formidable challenges in achieving effective treatments. Despite extensive research in computational methods for drug response prediction, achieving personalized treatment insights remains challenging. Emerging solutions combine multiple omics data, leveraging graph neural networks to integrate molecular interactions into the reasoning process. However, effectively modeling and harnessing this information, as well as gaining the trust of clinical professionals remain complex. This paper introduces ExplainMIX, a pioneering approach that utilizes directed graph neural networks to predict drug responses with interpretability. ExplainMIX adeptly captures intricate structures and features within directed heterogeneous graphs, leveraging diverse data modalities such as genomics, proteomics, and metabolomics. ExplainMIX goes beyond prediction by generating transparent and interpretable explanations. Incorporating edge-level, meta-path, and graph structure information, it provides meaningful insights into factors influencing drug response, supporting clinicians and researchers in the development of targeted therapies. Empirical results validate the efficacy of ExplainMIX in prediction and interpretation tasks by constructing a quantitative evaluation ground truth. This approach aims to contribute to precision medicine research by addressing challenges in interpretable personalized drug response prediction within the landscape of cancer.

A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements. As a result, computational methods are increasingly popular to be chosen. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved impressive performance. In this paper, we present a novel recognition algorithm named TeaTFactor that utilizes pre-training for the model training of TFs prediction. The model is built upon the BERT architecture, initially pre-trained using protein data from UniProt. Subsequently, the model was fine-tuned using the collected TFs data of tea plants. We evaluated four different word segmentation methods and the existing state-of-the-art prediction tools. According to the comprehensive experimental results and a case study, our model is superior to existing models and achieves the goal of accurate identification. In addition, we have developed a web server at http://teatfactor.tlds.cc, which we believe will facilitate future studies on tea transcription factors and advance the field of crop synthetic biology.

Inframe insertion/deletion (indel) variants may alter protein sequence and function, which are closely related to an extensive variety of diseases. Although recent researches have paid attention to the associations between inframe indels and diseases, modeling indels in silico and interpreting their pathogenicity remain challenging, mainly due to the lack of experimental information and computational methodologies. In this article, we propose a novel computational method named PredinID (Predictor for inframe InDels) via graph convolutional network (GCN). PredinID leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation, regarding the pathogenic inframe indel prediction as a node classification task. An edge-based sampling strategy is designed for extracting information from both the potential connections of feature space and the topological structure of subgraphs. Evaluated by 5-fold cross-validations, the PredinID method achieves satisfactory performance and is superior to four classic machine learning algorithms and two GCN methods. Comprehensive experiments show that PredinID has superior performances when compared with the state-of-the-art methods on the independent test set. Moreover, we also implement a web server at http://predinid.bio.aielab.cc/, to facilitate the use of the model.

BACKGROUND: MircoRNAs (miRNAs) play a central role in diverse biological processes of Camellia sinensis var.assamica (CSA) through their associations with target mRNAs, including CSA growth, development and stress response. However, although the experiment methods of CSA miRNA-target identifications are costly and time-consuming, few computational methods have been developed to tackle the CSA miRNA-target association prediction problem. RESULTS: In this paper, we constructed a heterogeneous network for CSA miRNA and targets by integrating rich biological information, including a miRNA similarity network, a target similarity network, and a miRNA-target association network. We then proposed a deep learning framework of graph convolution networks with layer attention mechanism, named MTAGCN. In particular, MTAGCN uses the attention mechanism to combine embeddings of multiple graph convolution layers, employing the integrated embedding to score the unobserved CSA miRNA-target associations. DISCUSSION: Comprehensive experiment results on two tasks (balanced task and unbalanced task) demonstrated that our proposed model achieved better performance than the classic machine learning and existing graph convolution network-based methods. The analysis of these results could offer valuable information for understanding complex CSA miRNA-target association mechanisms and would make a contribution to precision plant breeding.

The continuous use intention in users is vital to the development strategy of video sharing platforms, and it creates intensive competition among providers. At present, academic research on video sharing platform users focuses on analysis of social behavior in the videos from the perspectives of the online community, media communications, social network fatigue, and motivations to use the video sharing platform, or looks at video classification based on user-generated text data. However, from the perspectives of belongingness and intentions to continue using the danmaku video sharing platform, there is a lack of quantitative analysis. Therefore, this paper intends to enrich this part of the research based on the stimulus organism response (SOR) theory, adding perceived interactivity and belongingness to constitute structural equation modeling (SEM). This study investigates the mediating effect of belongingness and the influence from five dimensions of perceived interactivity on satisfaction and belonging, and goes even further to continuance intentions in the context of the danmaku video sharing platform. The results indicate that belongingness mediates the impact on continuance intention from control, playfulness, and responsiveness. Satisfaction relates positively to belongingness in danmaku video sharing platform users, which further significantly impacts their continuance intentions.

A change and maintenance method is proposed based on the change propagation model and the procedure model information for solving data maintenance problem of 3D machining procedure model change and to help improve the flexibility of 3D machining procedure model and the reliability of the change result. Design Structure Matrix (DSM) is established by analyzing the relationships between machining features in the machining process route to obtain all possible propagation paths. On the basis of obtained paths, machining features that may be affected and machining procedure models related to machining features are stored by the structured method. Algorithms to solve the problems of adding, deleting and modifying machining features are proposed to realize the change and maintenance of 3D machining procedure model by combining machining procedure model’s information in the machining process route. In the end, some numerical examples are given to explain both rationality and feasibility of the proposed approaches.

Sampling plays an important role in a variety of graphics applications. Among existing sampling methods, Poisson disk sampling is popular thanks to its useful statistical property in distribution and the absence of aliasing artifacts. Although many promising algorithms have been proposed for multi-dimensional sampling in Euclidean space, very few research studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This still remains a challenge due to the following reasons: first, a surface is a two-dimensional manifold that has arbitrary topology and complicated geometry, and is embedded in R3 or even higher dimensional space. Second, the exact geodesic distance should be used to enforce the minimum distance constraint between any pair of samples. Third, the algorithm should be parallelized such that it can make full use of all available threads. Last but not least, the generated samples should be randomly and uniformly distributed on surfaces, and exhibit the blue noise pattern without bias. Wei [2008] pioneered a parallel Poisson disk sampling algorithm by subdividing the sample domain into grid cells and drawing samples concurrently from multiple cells that are sufficiently far apart to avoid conflicts. Bowers et al. [2010] extended Wei's algorithm to 3D surfaces. Their method is highly efficient, allowing sampling on large-scale models at interactive speed. However, the generated distribution is not fully random since the sequence of processing the phase groups follows a predefined order. Moreover, the approximate geodesic computation in their approach results in large errors in models with rich features and thus compromises the sampling quality.