Essential protein discovery on weighted PPI networks via statistical information fusion

Identifying essential proteins is crucial for understanding disease mechanisms and developing therapeutic strategies. During the past several decades, numerous algorithms have been introduced to integrate topological and biological information to tackle the challenge of identifying essential proteins. However, existing methods still have some drawbacks: (1) the lack of rigorous mathematical interpretation for the parameters that determine their respective weights when integrating topological and biological information; (2) the lack of flexibility for adding or removing topological and biological information from integration process in real applications. To overcome these limitations, we propose a novel essential protein discovery method, called EPSIF, which assigns weights to PPI network interactions and integrates diverse topological and biological information via a statistical ensemble model. To assess the performance of EPSIF, we conduct experiments on real PPI networks and compare with eight state-of-the-art algorithms, which confirm the effectiveness and flexibility of EPSIF.