Shangying Wang

[Shangying Wang @shangying]

@nchembio @BakerJj12 We developed a machine learning iterative algorithm that allows the identification of factors to overexpress, culminating in a 137% increase in peroxisome functional capacity compared to a wild-type strain!

[Nature Chemical Biology @nchembio]

Here, @BakerJJ12 et al. improve on a strategy of compartmentalizing metabolic pathways to the peroxisome aided by machine learning and apply it geraniol production https://t.co/kJTG3L3NG7

[Shangying Wang @shangying]

Our latest review, 'AI in Cellular Engineering and Reprogramming' has just been published in the Biophysical Journal. We delve into the fascinating intersection of machine learning and biophysics, with a special focus on cellular engineering and reprogramming. Check it out here!

[Shangying Wang @shangying]

Please check our recent work on Gen Biotech @GENBiotechJrnl ! We developed a deep learning algorithm that can predict the conditional probability distribution of a phenotype of interest with no prior knowledge or assumption of the probability distributions.@Newcolour78 @sara_capp

[GEN Biotechnology @GENBiotechJrnl]

Issue #6 is here!
-Virus-like particles for diagnostic controls @PaulFreemont
-#CRISPR carbon capture @GrinsteinJ
-Science, art and vulnerability @samira_kiani1
-Deep learning for noisy biological systems @Newcolour78
..and more!

https://t.co/AEDNbw0Dku

[Shangying Wang @shangying]

A great breakthrough for engineering #CARTcell signaling domains using #synbio and #MachineLearning. Please check this out!

[Kyle G Daniels @ScienceKyle]

Our paper using #synbio and #MachineLearning to engineer #CARTcell signaling domains is out in Science (first release, final version is out next week).

@shangying @sara_capp @DrMilosSimic @HershBhargava @Newcolour78 @DamonRunyon

https://t.co/yNnGvGpHKp

[Kyle G Daniels @ScienceKyle]

Our paper using #synbio and #MachineLearning to engineer #CARTcell signaling domains is out in Science (first release, final version is out next week).

@shangying @sara_capp @DrMilosSimic @HershBhargava @Newcolour78 @DamonRunyon

https://t.co/yNnGvGpHKp

[Shangying Wang @shangying]

It is very nice to be able to meet so many researchers and discuss science in person after more than 18 months' virtual meetings due to pandemics.

[Shangying Wang @shangying]

AI-assisted Biology: Predict the Conditional Probability Distributions from Noisy Measurements https://t.co/DrNwGCNYLJ
A novel AI model to predict the probability distributions with limited measurements, which will be extremely useful for analyzing noisy biological data!

[Shangying Wang @shangying]

My first news&views of a recently published paper, which proposed a machine-learning-based sampling approach for multiscale simulations! Very cool work!
https://t.co/xkPIifCTvP

[Shangying Wang @shangying]

Very interesting research investigating branching pattern formation in P. aeruginosa by mathematical modeling and machine learning!

Collective colony growth is optimized by branching pattern formation in Pseudomonas aeruginosa | Molecular Systems Biology https://t.co/jnJgSXwk0a

[Shangying Wang @shangying]

@Newcolour78 I only met Frances once in person. She attended our lab meeting at Duke. She is such a great scientist and really helps us by giving lots of insightful suggestions! It's such an honor to be her mentee's mentee!

[Shangying Wang @shangying]

Massive acceleration by using neural networks to emulate mechanism-based... https://t.co/uaEc1BSGJi via @YouTube

[Shangying Wang @shangying]

Hi!