• 中国精品科技期刊
  • 《中文核心期刊要目总览》收录期刊
  • RCCSE 中国核心期刊(5/114,A+)
  • Scopus收录期刊
  • 美国《化学文摘》(CA)收录期刊
  • WHO 西太平洋地区医学索引(WPRIM)收录期刊
  • 《中国科学引文数据库(CSCD)》核心库期刊 (C)
  • 中国科技核心期刊
  • 中国科技论文统计源期刊
  • 《日本科学技术振兴机构数据库(中国)》(JSTChina)收录期刊
  • 美国《乌利希期刊指南》(UIrichsweb)收录期刊
  • 中华预防医学会系列杂志优秀期刊(2019年)

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Lasso降维策略下SIS与MDS在乳腺癌转录组数据机器学习建模中的比较

马瑄 张华麟 梁佳琪 杨开鑫 刘龙

马瑄, 张华麟, 梁佳琪, 杨开鑫, 刘龙. Lasso降维策略下SIS与MDS在乳腺癌转录组数据机器学习建模中的比较[J]. 中华疾病控制杂志, 2023, 27(11): 1360-1364. doi: 10.16462/j.cnki.zhjbkz.2023.11.019
引用本文: 马瑄, 张华麟, 梁佳琪, 杨开鑫, 刘龙. Lasso降维策略下SIS与MDS在乳腺癌转录组数据机器学习建模中的比较[J]. 中华疾病控制杂志, 2023, 27(11): 1360-1364. doi: 10.16462/j.cnki.zhjbkz.2023.11.019
MA Xuan, ZHANG Hualin, LIANG Jiaqi, YANG Kaixin, LIU Long. Application of a dimensionality reduction strategy based on SIS and MDS and machine learning statistical modeling methods to breast cancer transcriptome data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1360-1364. doi: 10.16462/j.cnki.zhjbkz.2023.11.019
Citation: MA Xuan, ZHANG Hualin, LIANG Jiaqi, YANG Kaixin, LIU Long. Application of a dimensionality reduction strategy based on SIS and MDS and machine learning statistical modeling methods to breast cancer transcriptome data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1360-1364. doi: 10.16462/j.cnki.zhjbkz.2023.11.019

Lasso降维策略下SIS与MDS在乳腺癌转录组数据机器学习建模中的比较

doi: 10.16462/j.cnki.zhjbkz.2023.11.019
基金项目: 

国家自然科学基金 81903418

国家自然科学基金 82173632

详细信息
    通讯作者:

    刘龙,E-mail:biostat-ll@sxmu.edu.cn

  • 中图分类号: R737.9;TP181

Application of a dimensionality reduction strategy based on SIS and MDS and machine learning statistical modeling methods to breast cancer transcriptome data

Funds: 

National Natural Science Foundation of China 81903418

National Natural Science Foundation of China 82173632

More Information
  • 摘要:   目的  探索以确定独立筛选(sure independence screening,SIS)与多维尺度变换(multi-dimensional scaling,MDS)为基础的2步降维策略,及以支持向量机(support vector machine,SVM)、随机森林(random forest,RF)和梯度推进机(gradient boosting machine,GBM)构建乳腺癌淋巴结转移风险预测的统计模型,为高危人群识别及早期干预提供科学依据。  方法  采用SIS和MDS作为初步降维方法,并以套索算法(least absolute shrinkage and selection operator,LASSO)为第2步降维方法,通过SIS+LASSO和MDS+LASSO的2步降维策略,将筛选的变量分别纳入SVM、RF和GBM 3种机器学习模型。使用受试者工作特征(receiver operating characteristic, ROC)曲线下面积(area under the curve,AUC)作为衡量模型预测性能的评价指标。  结果  所有预测模型中,SIS+LASSO和MDS+LASSO 2步降维策略相对SIS和MDS单步策略在SVM、RF和GBM 3种预测模型下预测稳定性提升,运行时间和运行内存减少。MDS+LASSO 2步降维策略相对于MDS单步降维策略的预测精度提升。所有策略中,GBM的预测精度均高于SVM和RF。  结论  在SIS与MDS基础上加入LASSO的2步降维策略,从运算速度、内存消耗、建模方法选择、预测精度等方面弥补了SIS和MDS单步降维的不足。对于不同的降维策略,GBM的预测性能均比SVM和RF好。
  • 图  1  研究流程图

    注:SIS, 确定独立性筛选;MDS, 多维缩放;LASSO, 最小绝对收缩和选择算法;SVM, 支持向量机;RF, 随机森林;GBM, 梯度推进机。

    Figure  1.  Research workflow

    Note: SIS, sure independent screening; MDS, multidimensional scaling; LASSO, least absolute shrinkage and selection operator; SVM, support vector machine; RF, random forest; GBM: gradient propulsion machine.

    图  2  SIS与SIS+LASSO在不同统计建模方法下的预测性能

    AUC:曲线下面积;SVM:支持向量机;RF:随机森林;GBM:梯度推进机;SIS:确定独立性筛选策略;LASSO: 套索算法; SIS+LASSO:确定独立性筛选和LASSO结合的策略。

    Figure  2.  Prediction performance of SIS and SIS+LASSO under different statistical modelling approaches

    AUC: area under the curve; SVM: support vector machine; RF: random forest; GBM: gradient propulsion machine; SIS: sure independent screening strategies; LASSO: least absolute shrinkage and selection operator; SIS+LASSO: the strategy of combining Sure Independent Screening and LASSO.

    图  3  MDS与MDS+LASSO在不同统计建模方法下的预测性能

    AUC:曲线下面积;SVM:支持向量机;RF:随机森林;GBM:梯度推进机;MDS:多维缩放;LASSO: 套索算法; MDS+LASSO:多维缩放和LASSO结合的策略。

    Figure  3.  Prediction performance of MDS and MDS+LASSO under different statistical modelling approaches

    AUC: area under the curve; SVM: support vector machine; RF: random forest; GBM: gradient propulsion machine; MDS: multidimensional scaling; LASSO: least absolute shrinkage and selection operator; MDS+LASSO: the strategy of combining multidimensional scaling and LASSO.

    表  1  不同降维策略下的AUC以及时间和内存消耗

    Table  1.   AUC, time and memory consumption under different dimension reduction strategies

    策略
    Strategy
    AUC 内存/KB
    Memory/KB
    时间/s
    Time/s
    SIS(88)+
      SVM 0.879(0.822, 0.933) 0.020 189.233
      RF 0.910(0.875, 0.952) 0.102 484.055
      GBM 0.917(0.880, 0.967) 4.020 380.851
    SIS+LASSO
      SVM 0.875(0.828, 0.928) 0.007 50.761
      RF 0.888(0.855, 0.929) 0.057 402.752
      GBM 0.895(0.861, 0.949) 3.833 97.875
    MDS(88)+
      SVM 0.735(0.684, 0.778) 0.018 210.714
      RF 0.879(0.846, 0.928) 0.145 641.008
      GBM 0.903(0.871, 0.947) 4.739 380.852
    MDS+LASSO
      SVM 0.873(0.821, 0.919) 0.006 56.422
      RF 0.885(0.860, 0.922) 0.108 608.742
      GBM 0.900(0.858, 0.943) 4.160 105.042
    注:1.AUC:曲线下面积;SIS: 确定独立筛选;LASSO: 套索算法; MDS:多维尺度变换;SVM:持向量机;RF:随机森林;GBM:梯度推进机。
    2.SIS(88)、MDS(88)表示该方法筛选出的变量数为88;SIS+LASSO:确定独立性筛选和LASSO结合的策略;MDS+LASSO:多维缩放和LASSO结合的策略。
    ①以[M (P25, P75)]表示。
    Note: 1. AUC: area under the curve; SIS:sure independence screening;LASSO: least absolute shrinkage and selection operator; MDS:multi-dimensional scaling;SVM:support vector machine;RF:random forest;GBM:gradient boosting machine.
    2.SIS (88) and MDS (88) indicate that the number of variables screened by this method is 88; SIS+LASSO: The strategy of combining Sure Independent Screening and LASSO; MDS+LASSO: The strategy of combining multidimensional scaling and LASSO.
    ① [M (P25, P75)].
    下载: 导出CSV
  • [1] Pfeiffer RM, Park Y, Kreimer AR, et al. Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies[J]. PLoS Med, 2013, 10(7): e1001492. DOI: 10.1371/journal.pmed.1001492.
    [2] Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021[J]. CA Cancer J Clin, 2021, 71(1): 7-33. DOI: 10.3322/caac.21654.
    [3] Woolston C. Breast cancer: 4 big questions[J]. Nature, 2015, 527(7578): S120-S120. DOI: 10.1038/527S101a.
    [4] Oreski D, Oreski S, Klicek B. Effects of dataset characteristics on the performance of feature selection techniques[J]. Appl Soft Comput, 2017, 52: 109-119. DOI: 10.1016/j.asoc.2016.12.023.
    [5] Frank SM, Qi A, Ravasio D, et al. Supervised learning occurs in visual perceptual learning of complex natural images[J]. Curr Biol, 2020, 30(15): 2995-3000.e3. DOI: 10.1016/j.cub.2020.05.050.
    [6] Aflalo Y, Dubrovina A, Kimmel R. Spectral generalized multi-dimensional scaling[J]. Int J Vis, 2016, 118(3): 380-392. DOI: 10.1007/s11263-016-0883-8.
    [7] 刘妍琛, 张晓曙, 崔旭东, 等. 基于Group LASSO Logistic回归分析模型分析流行性乙型脑炎早期临床症状与预后的关联[J]. 中华疾病控制杂志, 2021, 25(8): 891-897, 934. DOI: 10.16462/j.cnki.zhjbkz.2021.08.005.

    Liu YC, Zhang XS, Cui XD, et al. Study on the relationship between early clinical symptoms and prognosis of Japanese encephalitis: based on Group LASSO Logistic regression model[J]. Chin J Dis Control Prev, 2021, 25(8): 891-897, 934. DOI: 10.16462/j.cnki.zhjbkz.2021.08.005.
    [8] Dai P, Chang W, Xin Z, et al. Retrospective study on the influencing factors and prediction of hospitalization expenses for chronic renal failure in China based on random forest and LASSO regression[J]. Front Public Health, 2021, 9: 678276. DOI: 10.3389/fpubh.2021.678276.
    [9] Heo JN, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke[J]. Stroke, 2019, 50(5): 1263-1265. DOI: 10.1161/STROKEAHA.118.024293.
    [10] Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future[J]. Stroke Vasc Neurol, 2017, 2(4): 230-243. DOI: 10.1136/svn-2017-000101.
    [11] Ellis K, Kerr J, Godbole S, et al. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers[J]. Physiol Meas, 2014, 35(11): 2191-2203. DOI: 10.1088/0967-3334/35/11/2191.
    [12] Natekin A, Knoll A. Gradient boosting machines, a tutorial[J]. Front Neurorobot, 2013, 7: 21. DOI: 10.3389/fnbot.2013.00021.
    [13] Chin K, DeVries S, Fridlyand J, et al. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies[J]. Cancer Cell, 2006, 10(6): 529-541. DOI: 10.1016/j.ccr.2006.10.009.
  • 加载中
图(3) / 表(1)
计量
  • 文章访问数:  233
  • HTML全文浏览量:  79
  • PDF下载量:  16
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-09-09
  • 修回日期:  2022-12-07
  • 网络出版日期:  2023-11-20
  • 刊出日期:  2023-11-10

目录

    /

    返回文章
    返回