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Methodology for Good Machine Learning with Multi-Omics Data

delete2024-01-31
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PRE
AI
T
Thibaud Coroller
B
Berkman Sahiner *
A
Anup Amatya
A
Alexej Gossmann
K
Konstantinos Karagiannis
C
Conor Moloney
R
Ravi K. Samala
L
Luis V. Santana‐Quintero
N
Nadia Solovieff
C
Craig Wang
L
Laleh Amiri‐Kordestani
Q
Qian Cao
H
H. Kenny
R
Rosane Charlab
F
Frank H. Cross
T
Tingting Hu
R
Ruihao Huang
J
Jeffrey Kraft
P
Peter Krusche
Y
Yutong Li
Z
Zheng Li
I
Ilya Mazo
R
Rahul Paul
S
Susan Schnakenberg
P
P. Serra
S
Sean Smith
C
Chi Song
F
Fei Su
M
Mohit Tiwari
C
Colin Vechery
X
Xin Xiong
J
Juan Pablo Zarate
H
Hao Zhu
A
Arunava Chakravartty
Q
Qi Liu
N
Nicholas Petrick
J
Julie A. Schneider
M
Mark Walderhaug
E
Emmanuel Zuber
DOI:10.1002/cpt.3105delete
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摘要

摘要

En 中文
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as maintaining effective communication and following good data science practices, followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
Keyword:
RADIOMICS
RADIOGENOMICS

期刊

C
Clinical Pharmacology and Therapeutics
IF:
5.5
论文数:
8.6K
被引数:
1.9W

机构

U
us food & drug administration (fda)
学者数:
1.4W
论文数: 9.3K
被引数: 3
N
Novartis
学者数:
1.8W
论文数: 9.5K
被引数: 2.9K
C
center for biologics evaluation & research (cber)
学者数:
944
论文数: 608
被引数: 0
N
novartis usa
学者数:
5.1K
论文数: 3.1K
被引数: 7
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