Author

S. M. Schenstrom

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Biophysics· 1

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Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

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A. Stein· 1 shared K. Lindorff-Larsen· 1 shared M. Cagiada· 1 shared R. Hartmann-Petersen· 1 shared S. Bottaro· 1 shared S. Lindemose· 1 shared

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2022-2022 · peak 1/yr

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1 shared paperBiophysics

A. Stein

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1 paper

Recent·Most cited

BiophysicsbioRxivJul 14, 2022

Discovering functionally important sites in proteins

M. Cagiada, S. Bottaro, S. Lindemose…·K. Lindorff-Larsen

This paper introduces a machine learning method to predict functional sites in proteins by integrating sequence conservation and biophysical stability models, validated through experimental data on missense variants.

DOI 10.1038/s41467-023-39909-0

protein sequencemachine learning (classical)

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