Shuyi Jin

Shuyi Jin, Mengji Zhang, Meijie Wang et al.

Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing. While deep graph models have demonstrated success in modeling complex biological systems, existing approaches often fail to distinguish between positive and negative interactions, limiting their utility for precise pharmacological predictions. In this study, we propose a novel deep graph model, \textbf{RGCNTD} (Relational Graph Convolutional Network with Tensor Decomposition), designed to predict both polar (e.g., activation, inhibition) and non-polar (e.g., binding, affect) chemical-gene interactions. Our model integrates graph convolutional networks with tensor decomposition to enhance feature representation and incorporates a conflict-aware sampling strategy to resolve polarity ambiguities. We introduce new evaluation metrics, \textit{AUC\textsubscript{polarity}} and \textit{CP@500}, to assess the model's ability to differentiate interaction types. Experimental results demonstrate that \textbf{RGCNTD} outperforms baseline models, achieving superior classification accuracy and improved discrimination of polar edges. Furthermore, we analyze the impact of subgraph components on predictive performance, revealing that additional network structures do not always enhance accuracy. These findings highlight the importance of polarity-aware modeling in drug discovery and network pharmacology, providing a robust framework for predicting complex biological interactions.

Shuyi Jin, Lu Chen, Hongru Ding et al.

This study evaluates the performance of large language models (LLMs) and the HINT model in predicting clinical trial outcomes, focusing on metrics including Balanced Accuracy, Matthews Correlation Coefficient (MCC), Recall, and Specificity. Results show that GPT-4o achieves superior overall performance among LLMs but, like its counterparts (GPT-3.5, GPT-4mini, Llama3), struggles with identifying negative outcomes. In contrast, HINT excels in negative sample recognition and demonstrates resilience to external factors (e.g., recruitment challenges) but underperforms in oncology trials, a major component of the dataset. LLMs exhibit strengths in early-phase trials and simpler endpoints like Overall Survival (OS), while HINT shows consistency across trial phases and excels in complex endpoints (e.g., Objective Response Rate). Trial duration analysis reveals improved model performance for medium- to long-term trials, with GPT-4o and HINT displaying stability and enhanced specificity, respectively. We underscore the complementary potential of LLMs (e.g., GPT-4o, Llama3) and HINT, advocating for hybrid approaches to leverage GPT-4o's predictive power and HINT's specificity in clinical trial outcome forecasting.