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Shangying Wang

As seen in: Nature, Flipboard, Cell Press, Mary Ann Liebert, Inc.
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Articles

ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway

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By Jordan Baker, Jie Shi, Shangying Wang, Elena Mujica, Simone Bianco, Sara Capponi, John E. Dueber
| Flipboard
Why do we die? A Nobel Prize-winning biologist explains Venki Ramakrishnan explains the ageing process and the new technologies that might help us slow it down. Ageing is a big existential question and, as …
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AI in cellular engineering and reprogramming

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By Sara Capponi, San Jose, Shangying Wang
| Cell Press Verified
Get full text access Log in, subscribe or purchase for full access. References 1. Greener, J.G. ∙ Kandathil, S.M. ... A guide to machine learning for biologists Nat. Rev. Mol. Cell Biol. 2022; 23:40-55 2. Géron, A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 3. Goodfellow, I. ∙ Bengio, Y. ∙ Courville, A. Deep Learning 4. Darcy, A.M. ∙ Louie, A.K. ∙ Roberts, L.W. Machine learning and the profession of medicine JAMA. 2016; 315:551-552 5. Obermeyer, Z.
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Inferring Conditional Probability Distributions of Noisy Gene Expression from Limited Observations by Deep Learning

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By Shangying Wang, Sara Capponi, Simone Bianco
| Mary Ann Liebert, Inc.
GEN BiotechnologyVol. 1, No. 6 Research ArticlesFree Access Abstract Biological systems are inherently noisy so that two genetically identical cells in the exact same environment will sometimes behave in dramatically different ways. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic variables or categories per specific input condition.
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