Data-driven bioinformatics to disentangle cells within a tissue microenvironment

Jesper Tegner; David Gomez-Cabrero

doi:10.1016/j.tcb.2022.03.009

Data-driven bioinformatics to disentangle cells within a tissue microenvironment

Jesper Tegner, David Gomez-Cabrero

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

1 Scopus citations

Abstract

Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.

Original language	English (US)
Journal	Trends in Cell Biology
DOIs	https://doi.org/10.1016/j.tcb.2022.03.009
State	Published - Apr 13 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-18
Acknowledgements: J.N.T and D.G-C acknowledge support from King Abdullah University of Science and Technology.

ASJC Scopus subject areas

Cell Biology

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10.1016/j.tcb.2022.03.009

https://repository.kaust.edu.sa/bitstream/10754/676275/1/1-s2.0-S0962892422000812-main.pdf

Cite this

@article{d783366d012b43d4aa4fc886137770a8,

title = "Data-driven bioinformatics to disentangle cells within a tissue microenvironment",

abstract = "Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.",

author = "Jesper Tegner and David Gomez-Cabrero",

note = "KAUST Repository Item: Exported on 2022-04-18 Acknowledgements: J.N.T and D.G-C acknowledge support from King Abdullah University of Science and Technology.",

year = "2022",

month = apr,

day = "13",

doi = "10.1016/j.tcb.2022.03.009",

language = "English (US)",

journal = "Trends in Cell Biology",

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T1 - Data-driven bioinformatics to disentangle cells within a tissue microenvironment

AU - Tegner, Jesper

AU - Gomez-Cabrero, David

N1 - KAUST Repository Item: Exported on 2022-04-18 Acknowledgements: J.N.T and D.G-C acknowledge support from King Abdullah University of Science and Technology.

PY - 2022/4/13

Y1 - 2022/4/13

N2 - Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.

AB - Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.

UR - http://hdl.handle.net/10754/676275

UR - https://linkinghub.elsevier.com/retrieve/pii/S0962892422000812

U2 - 10.1016/j.tcb.2022.03.009

DO - 10.1016/j.tcb.2022.03.009

M3 - Article

C2 - 35430125

SN - 0962-8924

JO - Trends in Cell Biology

JF - Trends in Cell Biology

ER -

Data-driven bioinformatics to disentangle cells within a tissue microenvironment

Abstract

Bibliographical note

ASJC Scopus subject areas

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