ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.

"availability of data and materials the code and data supporting the conclusions of this article are openly accessible in the github repository at https://github com/lionkingahau/ecdep ."

"availability of data and materials the code and data supporting the conclusions of this article are openly accessible in the github repository at https://github com/lionkingahau/ecdep . it's important to note that all the machine learning and deep learning methods we compare undergo identical preprocessing steps thus ensuring the fairness and consistency of the experimental setup 11 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p=\left(1-\frac mn\right)^k$$\end{document} p = 1 - m n k for details on the experimental environment and package requirements please access our github repository ( https://github com/lionkingahau/ecdep ) or refer to the supplementary materials: table s6 ."

"Declarations Ethics approval and consent to participateNot applicable. Consent for publicationNot applicable. Competing interestsThe authors declare no competing interests. Competing interests The authors declare no competing interests."

"Funding This work was supported by funds from the Anhui Provincial Department of Education University Natural Science Research Project (2022AH040122, 2023AH040132), the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization (SKLTOF20150103). The funding agencies provided funds for the article processing fee and the corresponding author’s work on the research presented in this manuscript."