Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction

D Giardiello, AC Antoniou, L Mariani, DF Easton, Ewout Steyerberg

Research output: Contribution to journal › Article › Academic › peer-review

4 Citations (Scopus)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

@article{6255c58d87684550993a72e685f2244e,

title = "Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction",

author = "D Giardiello and AC Antoniou and L Mariani and DF Easton and Ewout Steyerberg",

year = "2020",

doi = "10.1186/s13058-020-1255-4",

language = "Undefined/Unknown",

volume = "22",

journal = "Breast Cancer Research",

issn = "1465-5411",

publisher = "BioMed Central Ltd.",

number = "1",

}

TY - JOUR

T1 - Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction

AU - Giardiello, D

AU - Antoniou, AC

AU - Mariani, L

AU - Easton, DF

AU - Steyerberg, Ewout

PY - 2020

Y1 - 2020

U2 - 10.1186/s13058-020-1255-4

DO - 10.1186/s13058-020-1255-4

M3 - Article

SN - 1465-5411

VL - 22

JO - Breast Cancer Research

JF - Breast Cancer Research

IS - 1

M1 - 17

ER -