Imperial College London
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ProfessorMichaelBronstein

Faculty of Engineering, Department of Computing

Visiting Professor
 
 
 
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Contact

 

m.bronstein Website

 
 
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Location

 

569Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Veselkov:2023:10.1038/s41598-023-41833-8,
author = {Veselkov, K and Southern, J and Gonzalez, Pigorini G and Pia, B and Poynter, L and Laponogov, I and Zhong, Y and Mirnezami, R and Veselkov, D and Bronstein, M},
doi = {10.1038/s41598-023-41833-8},
journal = {Scientific Reports},
pages = {1--9},
title = {Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient},
url = {http://dx.doi.org/10.1038/s41598-023-41833-8},
volume = {13},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe’s culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy.
AU  - Veselkov,K
AU  - Southern,J
AU  - Gonzalez,Pigorini G
AU  - Pia,B
AU  - Poynter,L
AU  - Laponogov,I
AU  - Zhong,Y
AU  - Mirnezami,R
AU  - Veselkov,D
AU  - Bronstein,M
DO  - 10.1038/s41598-023-41833-8
EP  - 9
PY  - 2023///
SN  - 2045-2322
SP  - 1
TI  - Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-023-41833-8
UR  - https://www.nature.com/articles/s41598-023-41833-8
UR  - http://hdl.handle.net/10044/1/106458
VL  - 13
ER  -
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