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Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes...

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Autores principales: Weiskittel, Taylor M., Cao, Andrew, Meng-Lin, Kevin, Lehmann, Zachary, Feng, Benjamin, Correia, Cristina, Zhang, Cheng, Wisniewski, Philip, Zhu, Shizhen, Yong Ung, Choong, Li, Hu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223789/
https://www.ncbi.nlm.nih.gov/pubmed/37242535
http://dx.doi.org/10.3390/ph16050752
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author Weiskittel, Taylor M.
Cao, Andrew
Meng-Lin, Kevin
Lehmann, Zachary
Feng, Benjamin
Correia, Cristina
Zhang, Cheng
Wisniewski, Philip
Zhu, Shizhen
Yong Ung, Choong
Li, Hu
author_facet Weiskittel, Taylor M.
Cao, Andrew
Meng-Lin, Kevin
Lehmann, Zachary
Feng, Benjamin
Correia, Cristina
Zhang, Cheng
Wisniewski, Philip
Zhu, Shizhen
Yong Ung, Choong
Li, Hu
author_sort Weiskittel, Taylor M.
collection PubMed
description Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.
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spelling pubmed-102237892023-05-28 Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms Weiskittel, Taylor M. Cao, Andrew Meng-Lin, Kevin Lehmann, Zachary Feng, Benjamin Correia, Cristina Zhang, Cheng Wisniewski, Philip Zhu, Shizhen Yong Ung, Choong Li, Hu Pharmaceuticals (Basel) Article Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights. MDPI 2023-05-16 /pmc/articles/PMC10223789/ /pubmed/37242535 http://dx.doi.org/10.3390/ph16050752 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Weiskittel, Taylor M.
Cao, Andrew
Meng-Lin, Kevin
Lehmann, Zachary
Feng, Benjamin
Correia, Cristina
Zhang, Cheng
Wisniewski, Philip
Zhu, Shizhen
Yong Ung, Choong
Li, Hu
Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms
title Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms
title_full Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms
title_fullStr Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms
title_full_unstemmed Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms
title_short Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms
title_sort network biology-inspired machine learning features predict cancer gene targets and reveal target coordinating mechanisms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223789/
https://www.ncbi.nlm.nih.gov/pubmed/37242535
http://dx.doi.org/10.3390/ph16050752
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