| Abstrakt: |
The study "Immune cell infiltration and prognostic index in cervical cancer: insights from metabolism-related differential genes" presents a new metabolism-related model for predicting the outcomes of cervical cancer patients. The model demonstrates significant prognostic value and is closely connected to immune cell infiltration and response to immunotherapy. However, the study acknowledges the need for further improvements in machine learning algorithms, bioinformatic evaluations, and validation through extensive clinical cohorts. The authors propose refining the model by optimizing the machine learning algorithm, utilizing bioinformatics technologies, and validating it with external clinical data. The study recognizes the limitations of bioinformatics research and suggests measures to address these deficiencies. The research was supported by the Natural Science Foundation of Shaanxi Province. [Extracted from the article] |
