PLoS Comput Biol April 2023 PMID 37079494

Bridging the gap between mechanistic biological models and machine learning surrogates.

Ioana M Gherman, Zahraa S Abdallah, Wei Pang, Thomas E Gorochowski, Claire S Grierson, Lucia Marucci

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Mentions

Peak: 9 in Apr 2023

across 2 sources since Sep 2022

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27,821

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Sep 2022 – Jul 2023

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AI synopsis

What people are saying

The reviews and discussions reflect an overall positive reception, with language like 'bridging the gap' and 'congrats' indicating support and recognition of the publication's importance; the tone suggests that these models are seen as promising tools for future biological research and disease understanding.

AgreementModerate agreement

Most discussions acknowledge the value and relevance of integrating mechanistic models with machine learning surrogates, as seen in supportive comments like 'big congrats' and mentions of the article's review of their applications.

InterestModerate interest

The posts demonstrate moderate interest, highlighting advancements in biological modeling and potential applications in neurodegenerative diseases, indicating engagement with the subject matter.

EngagementModerate engagement

Discussions show some depth, referencing specific concepts like the efficiency of ML surrogates and their role in understanding complex biological systems, suggesting active engagement.

ImpactModerate impact

Participants perceive positive implications, emphasizing the potential of these models to revolutionize biological research and medical applications, implying significant impact.

Abstract in brief

Mechanistic models have traditionally been used to illustrate complex biological processes, but their increasing complexity has raised computational challenges for simulations and real-time analysis.
Surrogate machine learning (ML) models can provide a more efficient approximation of these complex models, significantly reducing computational requirements once they are trained.
The paper reviews existing literature on ML surrogates, focusing on their design, training, and practical applications in biological modeling, suggesting their potential to enable complex biological system simulations on standard computer setups.

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All mentions

Integrating Biological Mechanistic Models with Machine Learning Surrogates

Mechanistic models illustrate complex biological processes but pose computational challenges. This review explores how machine learning surrogates can efficiently approximate these models, enabling simulations on…

Social RTCL TV · Jul 23, 2023 21 views

qyliang

@qyliang1 · X

RT @O_Borkowski: Bridging the gap between mechanistic biological models and machine learning surrogates https://t.co/rAKKFKH3iT https://t.c…

Jun 14, 2023 · 3 shares · 216 followers View post →

Brian Dean

@bdean_ · X

RT @O_Borkowski: Bridging the gap between mechanistic biological models and machine learning surrogates https://t.co/rAKKFKH3iT https://t.c…

Jun 13, 2023 · 3 shares · 560 followers View post →

☀️ ℝ

@rravi · X

RT @O_Borkowski: Bridging the gap between mechanistic biological models and machine learning surrogates https://t.co/rAKKFKH3iT https://t.c…

Jun 13, 2023 · 3 shares · 1,490 followers View post →

Olivier Borkowski

@O_Borkowski · X

Bridging the gap between mechanistic biological models and machine learning surrogates https://t.co/rAKKFKH3iT https://t.co/JzgixYTPG7

Jun 13, 2023 · 19 likes · 3 shares · 7,002 followers View post →

L Brielmaier

@BrielmaierL · X

#PLOSCompBio: Bridging the gap between mechanistic biological models and machine learning surrogates https://t.co/LUifluwVya Software, AI, ML & GPT are coming to the neurodegenerative diseases. Who will lead?

May 1, 2023 · 546 followers View post →

Bristol BioDesign Institute

@BrisBioDesign · X