Interpretable Link Prediction in AI-Driven Cancer Research: Uncovering Co-Authorship Patterns
This work addresses the problem of optimizing interdisciplinary team formation for researchers and policymakers in cancer research, though it is incremental as it applies existing methods to a specific domain.
The study tackled the challenge of predicting collaboration patterns in AI-driven cancer research by analyzing co-authorship networks from 7,738 publications, finding that random forest models achieved the highest recall and identified key factors like discipline similarity influencing collaboration types.
Artificial intelligence (AI) is transforming cancer diagnosis and treatment. The intricate nature of this disease necessitates the collaboration of diverse stakeholders with varied expertise to ensure the effectiveness of cancer research. Despite its importance, forming effective interdisciplinary research teams remains challenging. Understanding and predicting collaboration patterns can help researchers, organizations, and policymakers optimize resources and foster impactful research. We examined co-authorship networks as a proxy for collaboration within AI-driven cancer research. Using 7,738 publications (2000-2017) from Scopus, we constructed 36 overlapping co-authorship networks representing new, persistent, and discontinued collaborations. We engineered both attribute-based and structure-based features and built four machine learning classifiers. Model interpretability was performed using Shapley Additive Explanations (SHAP). Random forest achieved the highest recall for all three types of examined collaborations. The discipline similarity score emerged as a crucial factor, positively affecting new and persistent patterns while negatively impacting discontinued collaborations. Additionally, high productivity and seniority were positively associated with discontinued links. Our findings can guide the formation of effective research teams, enhance interdisciplinary cooperation, and inform strategic policy decisions.