Abstract
To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-coding RNA in the transcriptome that has a significant impact on clinical prognosis survival analysis was initially screened. Then use a random forest ensemble learning algorithm to train it to get a preliminary model. Finally, based on the optimized random survival forest model, the Cox regression model was once again integrated, and the risk values of the two were integrated. The RCCT (Random-Cox Combined to Survival) method was proposed to provide clinical decision-makers certain reference values.
Highlights
The survival analysis method refers to the method of solving specific problems related to survival, and it is an important theoretical basis for establishing a survival prediction model and analyzing survival prediction ability
The RCCT method is based on the random survival forest model of the integrated learning method and is obtained by integrating the Cox survival model again
This research established a survival analysis prediction model based on ensemble learning methods
Summary
The survival analysis method refers to the method of solving specific problems related to survival, and it is an important theoretical basis for establishing a survival prediction model and analyzing survival prediction ability. This research is based on the existing survival analysis model, combined with machine learning, especially ensemble learning methods, using rectal cancer and colon cancer data sets[8,9] to construct a survival prediction model. On this basis, the proportional hazard model and the random forest survival model were once again integrated, and the RCCT method was proposed, and a more significant lncRNA was found for survival prediction of clinical prognosis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
