Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

IF 6.1 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Alphonse Houssou Hounye, Li Xiong, Muzhou Hou
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引用次数: 0

Abstract

To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, ten different machine learning models were utilized to predict mortality across various datasets. Then we have utilized the important features identified by those machines learning methods to construct a new model named NKECLR to forecast mortality of patient with cancer. To explicitly clarify the model's decision-making process and uncover novel findings, an interpretable technique incorporating machine learning and SHapley Additive exPlanations (SHAP), as well as LIME, has been employed, and four genes EPGN, PHF11, RBM34, and ZFP36 were identified from those machine learning(ML). The experimental analysis conducted on training and validation datasets demonstrated that the proposed model has a good performance com- pared to existing methods with AUC value 81.8%, and 79.3%, respectively. Moreover, when combined our NKECLR with PD-L1, PD-1, and CTLA-4 the AUC value was 83%0. Finally, these findings have been applied to comprehend the response of drugs and immunotherapy. Our research introduced an innovative predictive NKECLR model utilizing natural killer(NK) cell marker genes for cohorts with melanoma cancer. The NKECLR model can effectively predict the survival of melanoma cancer cohorts and treatment results, revealing distinct immune cell infiltration in the high-risk group.

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来源期刊
Apoptosis
Apoptosis 生物-生化与分子生物学
CiteScore
9.10
自引率
4.20%
发文量
85
审稿时长
1 months
期刊介绍: Apoptosis, a monthly international peer-reviewed journal, focuses on the rapid publication of innovative investigations into programmed cell death. The journal aims to stimulate research on the mechanisms and role of apoptosis in various human diseases, such as cancer, autoimmune disease, viral infection, AIDS, cardiovascular disease, neurodegenerative disorders, osteoporosis, and aging. The Editor-In-Chief acknowledges the importance of advancing clinical therapies for apoptosis-related diseases. Apoptosis considers Original Articles, Reviews, Short Communications, Letters to the Editor, and Book Reviews for publication.
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