EssTFNet: Integration of Adaptive Time–Frequency and DNA Language Models for Interpretable Human Essential Gene Prediction

Abstract Essential genes are defined as those that are indispensable for an organism’s survival. The loss of function of these genes results in cell death or an inability to complete the normal life cycle. Research on essential genes is pivotal in elucidating the origin and evolution of life, as well as in identifying potential therapeutic targets. Therefore, accurately predicting essential genes is of great scientific importance and has many applications in basic research and the biomedical field. In this study, we propose EssTFNet, a novel and interpretable deep learning framework that combines adaptive time–frequency analysis with a DNA language model to achieve accurate prediction of human essential genes while enabling mechanistic biological interpretation. Specifically, EssTFNet leverages the architecture of ATFNet, which innovatively maps DNA and protein sequences into equivalent time-series signals to extract periodic and non-stationary features, thereby enhancing the model’s capacity to capture complex sequence patterns. Through effective feature selection and architectural optimization, EssTFNet strikes a favorable balance among prediction accuracy, model interpretability, and cross-tissues generalization capability, significantly outperforming current mainstream sequence-based deep learning methods with an AUC of 0.8336 and an AUPR of 0.8212. Additionally, the DeepLIFT attribution method was employed to identify functional motifs closely associated with gene essentiality, offering valuable insights for experimental validation. For the convenience of researchers, we have developed an easy-to-use web server and made it along with the source code in a GitHub repository: https://github.com/QIANJINYDX/EssTFNet. Overall, this study presents a potentially useful methodological framework for human essential genes prediction, which could provide valuable insights for future research and applications in this field. Competing Interest Statement The authors have declared no competing interest. Footnotes Dong-Xin Ye: 202512140620{at}std.uestc.edu.cn, Shi-Shi Yuan: 626164476{at}qq.com, Wei Su: wsu{at}std.uestc.edu.cn, Hong-Qi Zhang: 1012883622{at}qq.com, Rui Li: 202321140412{at}std.uestc.edu.cn, Ye-Chen Qi: ycqi{at}uestc.edu.cn