Interpretable Link Prediction in AI-Driven Cancer Research: Uncovering Co-Authorship Patterns

TL;DR

This study uses interpretable machine learning to analyze co-authorship networks in AI-driven cancer research, revealing key factors influencing collaboration patterns to optimize team formation and policy decisions.

Contribution

It introduces an interpretable machine learning approach to predict and understand co-authorship patterns in interdisciplinary cancer research.

Findings

Discipline similarity positively influences new and persistent collaborations.

High productivity and seniority are linked to discontinued links.

Random forest achieved highest recall in predicting collaboration types.

Abstract

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.