Can Omics Help in Prognostic Machine Learning Interpretability?

被引:0
|
作者
Uche, C. Z. [1 ]
Caruana, R. [2 ]
Lee, S. H. [1 ]
Geng, H. [1 ]
Wright, C. M. [1 ]
Xiao, Y. [1 ]
机构
[1] Univ Penn, Dept Radiat Oncol, Philadelphia, PA 19104 USA
[2] Microsoft Res, Redmond, WA USA
关键词
D O I
暂无
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
2215
引用
收藏
页码:E124 / E125
页数:2
相关论文
共 50 条
  • [1] Machine Learning: How It Can Help Nanocomputing
    Uusitalo, Mikko A.
    Peltonen, Jaakko
    Ryhaenen, Tapani
    [J]. JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE, 2011, 8 (08) : 1347 - 1363
  • [2] Can Machine Learning Help Fight Fake News?
    Kompella, Kashyap
    [J]. ECONTENT, 2017, 40 (05) : 40 - 40
  • [3] Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning
    Krishnan M.
    [J]. Philosophy & Technology, 2020, 33 (3) : 487 - 502
  • [4] A Study on Interpretability of Decision of Machine Learning
    Shirataki, Shohei
    Yamaguchi, Saneyasu
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2017, : 4830 - 4831
  • [5] A Review of Framework for Machine Learning Interpretability
    Araujo, Ivo de Abreu
    Torres, Renato Hidaka
    Sampaio Neto, Nelson Cruz
    [J]. AUGMENTED COGNITION, AC 2022, 2022, 13310 : 261 - 272
  • [6] Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics
    Li, Wanrong
    Xia, Yuren
    Wang, Jian
    Jin, Hao
    Li, Xin
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [7] Interpretability in HealthCare: A Comparative Study of Local Machine Learning Interpretability Techniques
    El Shawi, Radwa
    Sherif, Youssef
    Al-Mallah, Mouaz
    Sakr, Sherif
    [J]. 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 2019, : 275 - 280
  • [8] Predicting LoRaWAN Behavior: How Machine Learning Can Help
    Cuomo, Francesca
    Garlisi, Domenico
    Martino, Alessio
    Martino, Antonio
    [J]. COMPUTERS, 2020, 9 (03) : 1 - 18
  • [9] Interpretability in healthcare: A comparative study of local machine learning interpretability techniques
    ElShawi, Radwa
    Sherif, Youssef
    Al-Mallah, Mouaz
    Sakr, Sherif
    [J]. COMPUTATIONAL INTELLIGENCE, 2021, 37 (04) : 1633 - 1650
  • [10] Interpretability and Reproducability in Production Machine Learning Applications
    Ghanta, Sindhu
    Subramanian, Sriram
    Sundararaman, Swaminathan
    Khermosh, Lior
    Sridhar, Vinay
    Arteaga, Dulcardo
    Luo, Qianmei
    Das, Dhananjoy
    Talagala, Nisha
    [J]. 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, : 658 - 664