Random Forest-Based Protein Model Quality Assessment (RFMQA) Using Structural Features and Potential Energy Terms

Open Access

Peer-reviewed

Research Article

Balachandran Manavalan,

Affiliation Center for In Silico Protein Science, School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
⨯
Juyong Lee,

Affiliation Center for In Silico Protein Science, School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
⨯
Jooyoung Lee

* E-mail: jlee@kias.re.kr

Affiliation Center for In Silico Protein Science, School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
⨯

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Protein structure prediction
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Protein structure
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Potential energy
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Machine learning
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Structural proteins
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Forecasting
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Protein structure comparison
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