Probation is a crucial institution in modern criminal law, embodying the principles of fairness and justice while contributing to the harmonious development of society. Despite its importance, the current Intelligent Judicial Assistant System (IJAS) lacks dedicated methods for probation prediction, and research on the underlying factors influencing probation eligibility remains limited. In addition, probation eligibility requires a comprehensive analysis of both criminal circumstances and remorse. Much of the existing research in IJAS relies primarily on data-driven methodologies, which often overlooks the legal logic underpinning judicial decision-making. To address this gap, we propose a novel approach that integrates legal logic into deep learning models for probation prediction, implemented in three distinct stages. First, we construct a specialized probation dataset that includes fact descriptions and probation legal elements (PLEs). Second, we design a distinct probation prediction model named the Multi-Task Dual-Theory Probation Prediction Model (MT-DT), which is grounded in the legal logic of probation and the Dual-Track Theory of Punishment. Finally, our experiments on the probation dataset demonstrate that the MT-DT model outperforms baseline models, and an analysis of the underlying legal logic further validates the effectiveness of the proposed approach.

Combination therapy is widely used to treat complex diseases, especially in patients who respond poorly to monotherapy. Identifying synergistic drug combinations (SDCs) poses a significant challenge due to the combinatorial complexity. Although graph representation learning algorithms have achieved success in predicting SDCs, predefined and rigid methods limit the capability of extracting complex semantic information, and high-order information within drug synergy data has not been adequately integrated with heterogeneous graph information. To address these challenges, we propose a novel framework, AMHP-SDC, which combines adaptive semantic meta-graph learning, hypergraph learning, and drug molecular graph perception for SDCs prediction. The proposed method consists of four main components: (1) A Heterogeneous graph Information Extraction (HIE) module that automatically aggregates semantic information through adaptive semantic meta-graph searching; (2) A Biochemical Information Extraction (BIE) module that integrates hypergraph representation with GeniePath and MLP to generate node embeddings; (3) An Information Fusion module that combines multi-source information to produce a final representation; and (4) A Synergy Prediction module that constructs both primary and auxiliary tasks for SDCs prediction. Computational experiment results demonstrate that AMHP-SDC outperforms the baseline methods on two benchmark datasets, confirming its effectiveness in predicting SDCs. Our code is available at: https://github.com/FranCHEN0520/AMHP-SDC.

Drug discovery plays a critical role in disease treatment, improving human health, and advancing medical progress, serving as the foundation for the development of new therapies and addressing global health challenges. Traditional clinical drug testing methods are time-consuming and labor-intensive. In contrast, current computational approaches enhance efficiency, but most studies treat drug-target and drug-disease interactions as independent processes, overlooking the ternary relationships among drugs, targets, and diseases. Additionally, many studies neglect the similarities between entities of the same type, such as between drugs, which have been shown to be closely related to mechanisms of action (MOAs). To address this issue, we propose a novel method — the Multidimensional Information Aggregated Heterogeneous Hypergraph Neural Network (MiaHHGNN). MiaHHGNN naturally utilizes hypergraphs to represent ternary relationships, enabling more effective modeling of drug-target-disease interactions. Additionally, MiaHHGNN learns the relationships between entities of the same type by incorporating natural similarity, which is based on molecular structural and Medical Subject Headings (MeSH) terms, and this significantly improves prediction accuracy. On multiple datasets, MiaHHGNN demonstrates outstanding performance in five-fold cross-validation. Additionally, the top-k analysis indicates that the model has practical applicability, and the ablation experiments demonstrate the effectiveness of each component. Our code is available at the link: https://github.com/predatoranimal/MiaHHGNN.

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse drug reactions with significant implications for patient safety and healthcare outcomes. While graph-based methods have achieved strong predictive performance, most approaches treat drug pairs independently, overlooking the complex, context-dependent interactions unique to drug pairs. Additionally, these models struggle to integrate biological interaction networks and molecular-level structures to provide meaningful mechanistic insights. In this study, we propose MolecBioNet, a novel graph-based framework that integrates molecular and biomedical knowledge for robust and interpretable DDI prediction. By modeling drug pairs as unified entities, MolecBioNet captures both macro-level biological interactions and micro-level molecular influences, offering a comprehensive perspective on DDIs. The framework extracts local subgraphs from biomedical knowledge graphs and constructs hierarchical interaction graphs from molecular representations, leveraging classical graph neural network methods to learn multi-scale representations of drug pairs. To enhance accuracy and interpretability, MolecBioNet introduces two domain-specific pooling strategies: context-aware subgraph pooling (CASPool), which emphasizes biologically relevant entities, and attention-guided influence pooling (AGIPool), which prioritizes influential molecular substructures. The framework further employs mutual information minimization regularization to enhance information diversity during embedding fusion. Experimental results demonstrate that MolecBioNet outperforms state-of-the-art methods in DDI prediction, while ablation studies and embedding visualizations further validate the advantages of unified drug pair modeling and multi-scale knowledge integration. Moreover, the pooling strategies yield interpretable insights into the underlying interaction mechanisms, highlighting MolecBioNet's potential as a robust and transparent framework for predictive modeling in support of drug safety assessment.

Drug combinations are vital for treating complex diseases and advancing drug development, but accurately identifying synergistic combinations remains a significant challenge. Although graph neural networks (GNNs) have recently been used to predict drug combinations, the complex interactions between drugs and multimodal data (e.g., target proteins) and the prevalent high-order relations among drugs have yet to be fully exploited. The hypergraph offers a natural methodology for modeling high-order relations and provides profound insights for multimodal fusion. Here, we introduce the multimodal relational hypergraph neural network (MRHGNN), a novel framework for predicting synergistic drug combinations. Specifically, we design a dual-channel architecture to capture the physicochemical attributes of drugs and their interactive synergies, thereby facilitating the generation of multimodal drug representations. To obtain comprehensive representations of drugs, we use an attention mechanism to explore complementarity among multimodal drug embeddings. In addition, the unified framework jointly learns primary and self-supervised learning tasks, fostering a robust predictive capability. Experimental results demonstrate that MRHGNN accurately predicts synergistic drug combinations, and the effectiveness of the dual-channel setup and motif structures has been validated through ablation studies. Further literature searches illustrate that our model holds significant promise in accelerating the discovery of novel synergistic drug combinations, particularly in cancer therapy. This study not only introduces a novel computational tool but also paves the way for advanced methodologies in drug discovery and development.

The dissemination of information and the development of public opinion are essential elements of most social networks and are often described as distinct, man-made occurrences. However, what is often disregarded is the interdependence between these two phenomena. Information dissemination serves as the foundation for the formation of public opinion, while public opinion, in turn, drives the spread of information. In our study, we model the coevolutionary relationship between information and public opinion on heterogeneous multiplex networks. This model takes into account a minority of individuals with steadfast opinions and a majority of individuals with fluctuating views. Our findings reveal the equilibrium state of public opinion in this model and examine the consistency between opinions and the network structure. Additionally, by identifying a linear relationship between mainstream public opinion and extreme individuals, we propose a strategy for regulating public opinion by adjusting the positions of extreme groups.

When facing choices such as getting vaccinated, traveling, and going to the cinema, individuals can be classified into two groups based on their decision-making strategy: 1) selfish agents, who make decisions to maximize their own payoff; and 2) collectivist agents, who try to maximize the group’s benefits. We model both strategies in the evolutionary dynamics of signed networks to examine how both types of agents choose to behave in society. In particular, we run simulations and find that when agents’ strategies are fixed, collectivist agents or group decision-making are conducive to cooperation. Some network properties such as having structural balance and a clear modular organization promote cooperation. However, when strategic learning is considered, the dynamics become highly uncertain. When collectivist agents are initially located in a closely related group, the cooperation ratio and proportion of collectivist agents both rise. These observations reveal that besides network properties, the topology distribution of agents with different decision-making tendencies plays a significant role in social evolution.