Predicting drug-disease associations (DDAs) is a crucial task in drug discovery and repositioning, as it enables the identification of novel therapeutic indications for existing drugs. However, current technologies for predicting these associations face limitations, such as challenges in data integration, difficulty in capturing multi-relational biological interactions, and limited generalizability. To address these issues, we present a network-based framework that integrates heterogeneous biological data-including drugs, proteins, biological functions, and diseases-into a unified graph structure. To effectively model the complex and multi-relational nature of this network, we employ a Relational Graph Convolutional Network (RGCN), which explicitly captures various types of biological interactions. Our model named DDA-RGCN achieves superior predictive performance, with an AUC of 0.9745 and an AUPR of 0.9728, outperforming several baseline and state-of-the-art methods. Case studies further demonstrate the model's potential in uncovering novel drug indications, supported by literature evidence. These findings underscore the value of combining heterogeneous biological networks with graph neural network techniques and highlight the potential of our RGCN-based approach to advance computational drug repositioning and support personalized medicine initiatives.

In the field of software engineering, the automated analysis of log data is crucial for operations and maintenance teams. This study introduces LLMDPP, a novel log parser that leverages Large Language Models (LLMs) and Determinantal Point Process (DPP) sampling techniques to enhance the efficiency and accuracy of online log parsing. LLMDPP transforms raw log messages into structured log templates through a few-shot learning, thus simplifying the processing and analysis of log data. The study explores the accuracy of LLMDPP in log-parsing tasks and compares the effectiveness of different encoding functions (TF-IDF and Flan-T5-small embedding layer) in DPP sampling. Experimental results show that LLMDPP outperforms traditional methods in both Global Accuracy and Parsing Accuracy, with the semantic information encoding function performing better when the number of samples is low. Furthermore, we simulate an online scenario to evaluate the parsing effectiveness of different sampling methods on unseen log datasets. The results indicate that the DPP sampling method has an advantage in maintaining sample diversity and fairness, which can improve parsing accuracy.

Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating/inhibiting mechanisms.

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem.