Integrating topology and biological information to predict essential proteins via Shannon entropy
Yan Liu, Zhong Wang, Zengyou He, Hexin Zhang, Jing Qin · 2024
Identifying essential proteins is vital for deciphering the intricacies of disease mechanisms and devising efficacious therapeutic strategies. Over the past several decades, a plethora of algorithms have been proposed, aimed at synthesizing topological and biological information to address the complex challenge of essential protein identification. Nevertheless, a critical examination of the current methodologies reveals certain limitations: (1) the aggregation of diverse features in various methods often requires parameter tuning to maintain balance, potentially introducing instability and increasing complexity in practical scenarios; (2) traditional methods commonly combine various features without in-depth mathematical or physical interpretation, possibly falling short of fully revealing the principles behind the observed phenomena. Hence, we propose a new algorithm for essential protein detection, which is named ITBSE. The basic idea behind this method is to reconstruct the PPI network by removing false positive edges and the subsequent allocation of a protein score. This scoring process integrates both the topological attributes of the reconstructed PPI network and biological data, utilizing the computational framework of Shannon entropy for a comprehensive assessment. To evaluate the effectiveness of our method, we conduct the experiments on real PPI networks and compare with 10 popular methods including DC, BC, CC, LID, PR, DMNC, LAC, NC, PeC and esPOS. The comparison results demonstrate that ITBSE is able to achieve better performance than those competing algorithms.