癌症转移预测和基因组生物标志物识别通过机器学习和可解释的人工智能在乳腺癌研究
Burak Yagin1, Fatma Hilal Yagin1, Cemil Colak1
1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Turkey.
Diagnostics (Basel, Switzerland)
|November 14, 2023
概括
这项研究开发了一种机器学习模型,用于预测乳腺癌转移,识别关键的基因组生物标志物. 该模型实现了96%的准确性,可能改善患者治疗策略.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 乳腺癌 (BC) 转移是一个重大的临床挑战.
- 预测转移并了解其基因组驱动因素对于有效治疗至关重要.
研究的目的:
- 使用机器学习 (ML) 和可解释的人工智能 (XAI) 开发乳腺癌转移的预测模型.
- 确定与乳腺癌患者转移相关的基因组生物标志物.
主要方法:
- 从98个原发性乳腺癌样本中分析基因组数据.
- 弹性网用于特征选择的应用,以识别基因组生物标志物.
- 多个ML算法 (LightGBM,CatBoost,XGBoost,GBT,AdaBoost) 的培训和评估.
- 使用夏普利添加式解释 (XAI) 来实现模型的可解释性.
主要成果:
- 轻GBM模型表现出高预测性能,准确率为96%,AUC为99.3%.
- 确定了表达增加的特定基因 (例如,TSPYL5,ATP5E) 与更高的转移风险相关.
- 已识别的基因表达减少 (例如,CACTIN,TGFB3) 也与转移风险增加有关.
结论:
- 开发的ML/XAI模型准确预测乳腺癌转移.
- 已识别的基因组生物标志物可以为个性化治疗策略提供信息.
- 这些发现可能有助于预防疾病的进展,并改善患者的治疗结果.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
09:53Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
Published on: August 16, 2020
7.3K
相关概念视频
Metastasis
5.6K
Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
5.6K
Cancer Survival Analysis
357
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
357
Mouse Models of Cancer Study
5.6K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.6K
