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Abstract
We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data. When a user-specified model is unavailable, global nonlinearities (epistasis) can be estimated from the data. MoCHI also leverages ensemble, background-averaged epistasis to learn sparse models that can incorporate higher-order epistatic terms. MoCHI is freely available as a Python package (
https://github.com/lehner-lab/MoCHI
) relying on the PyTorch machine learning framework and allows biophysical measurements at scale, including the construction of allosteric maps of proteins.
Academic Publication MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data
Research Abstract & Technology Focus
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MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data
Abstract We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction te...
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Frequently Asked Questions (FAQ)
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What is the core focus of the research titled 'MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data'?
This literature focuses on: Abstract We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysica...
Are there open-source GitHub repositories related to MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data?
Yes, open-source projects like Krishnagangwal/CS-Fundamentals (Curated CS fundamentals for placement prep: DSA,Computer Networks, DBMS & SQL, OOPs, Operating Systems, System Design & Software Engineering) are actively building upon these concepts.
Which startups are commercializing the technology behind MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data?
Products like Alumni Founder are bringing this to market. Their focus is: The tool that maps founder networks for any company.
What other academic literature is closely related to 'MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data'?
Yes, highly correlated activity was mapped. An entry titled 'MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data' discusses this: Abstract We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI inf...
Are there commercial applications of 'MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data' in market news publications?
Yes, highly correlated activity was mapped. An entry titled 'The Fast Lane Hypothesis: Von Economo Neurons Implement a Biological Speed-Accuracy Tradeoff' discusses this: Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of spec...
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GitHubKrishnagangwal/CS-Fundamentals
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