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@article{Ashrafian:2020:10.3389/fonc.2019.01527,
author = {Ashrafian, H and Goodman, J},
doi = {10.3389/fonc.2019.01527},
journal = {Frontiers in Oncology},
title = {The promising connection between data science and evolutionary theory in oncology},
url = {http://dx.doi.org/10.3389/fonc.2019.01527},
volume = {9},
year = {2020}
}

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TY  - JOUR
AB  - Theoretical and empirical work over the past several decades suggests that oncogenesisand disease progression represents an evolutionary story. Despite this knowledge,current anti-resistance strategies to drugs are often managed through treating cancersas independent biological agents divorced from human activity. Yet once drug resistanceto cancer treatment is understood as a product of artificial or anthropogenic ratherthan unconscious selection, oncologists could improve outcomes for their patients byconsulting evolutionary studies of oncology prior to clinical trial and treatment plan design.In the setting of multiple cancer types, for example, a machine learning algorithm canpredict the genetic changes known to be related to drug resistance. In this way, a unitybetween technology and theory might have practical clinical implications—and may pavethe way for a new paradigm shift in medicine.
AU  - Ashrafian,H
AU  - Goodman,J
DO  - 10.3389/fonc.2019.01527
PY  - 2020///
SN  - 2234-943X
TI  - The promising connection between data science and evolutionary theory in oncology
T2  - Frontiers in Oncology
UR  - http://dx.doi.org/10.3389/fonc.2019.01527
UR  - http://hdl.handle.net/10044/1/76475
VL  - 9
ER  -

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