通过集合机器学习算法为非小细胞肺癌患者预测个性化医疗中的治疗建议

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2024-10-14 eCollection Date: 2024-01-01 DOI:10.1177/11769351241272397
Hojin Moon, Lauren Tran, Andrew Lee, Taeksoo Kwon, Minho Lee
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引用次数: 0

摘要

目标:本研究的主要目标是通过整合各种机器学习算法,开发与治疗相关的非小细胞肺癌基因组预测标记物,从而为患者推荐近乎最佳的个体化化疗方案,努力实现疗效最大化或治疗相关毒性最小化。这项研究有助于开发更精细、更准确、更有效的疗法,满足患者的特殊需求:为了实现我们的研究目标,我们采用了集合学习算法,通过随机生存森林(Random Survival Forests)对正则化考克斯回归模型和基于树的非参数模型进行装袋。我们从NCBI基因表达总库中为肺癌患者建立了一个全面的元数据库,以捕捉和利用复杂的基因组模式,从而更准确地预测治疗结果:结果:所开发的新型预测算法能够支持治疗 NSCLC 的复杂临床决策过程。它有效地解决了患者的异质性问题,提供了精细化和个性化的预测,提高了为符合条件的患者开具化疗方案的精确性:这项研究通过提高化疗的准确性和疗效,为需要正确治疗的目标患者群体提供化疗方案,从而为癌症治疗的实质性进步做出贡献。复杂的机器学习技术与基因组数据的整合为临床决策提供了强有力的支持,从而为改变当前的癌症治疗模式带来了巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine.

Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. This research can contribute toward developing a more refined, accurate and effective therapy accounting for specific patient needs.

Methods: To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately.

Results: The developed novel prediction algorithm demonstrates the ability to support complex clinical decision-making processes in the treatment of NSCLC. It effectively addresses patient heterogeneity, offering predictions that are both refined and personalized in improving the precision of chemotherapy regimens prescribed to the eligible patients.

Conclusion: This research should contribute substantial advancement of cancer treatments by improving the accuracy and efficacy of chemotherapy treatments for a targeted group of patients who need the right treatment. The integration of complex machine learning techniques with genomic data holds substantial potential to transform current cancer treatment paradigms by providing robust support in clinical decision-making.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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