Prediction of whole-cell transcriptional response with machine learning

Prediction of whole-cell transcriptional response with machine learning

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations...

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Bibliographic Details
Main Authors: Eslami, Mohammed, Borujeni, Amin Espah, Eramian, Hamed, Weston, Mark, Zheng, George, Urrutia, Joshua, Corbet, Carolyn, Becker, Diveena, Maschhoff, Paul, Clowers, Katie, Cristofaro, Alexander, Hosseini, Hamid Doost, Gordon, D Benjamin, Dorfan, Yuval, Singer, Jedediah, Vaughn, Matthew, Gaffney, Niall, Fonner, John, Stubbs, Joe, Voigt, Christopher A, Yeung, Enoch
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering
Format: Article
Language:English
Published: Oxford University Press (OUP) 2023
Online Access:https://hdl.handle.net/1721.1/147936

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https://hdl.handle.net/1721.1/147936

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