906 citations · 909 across the 4 of their papers we have counts for
4 papers
Deep generative models of genetic variation capture mutation effects
Adam J. Riesselman, John B. Ingraham, Debora S. Marks
The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on…
Variational Inference for Sparse and Undirected Models
John Ingraham, Debora Marks
Undirected graphical models are applied in genomics, protein structure prediction, and neuroscience to identify sparse interactions that underlie discrete data. Although Bayesian m…
Quantification of the effect of mutations using a global probability model of natural sequence variation
Thomas A. Hopf, John B. Ingraham, Frank J. Poelwijk +3
Modern biomedicine is challenged to predict the effects of genetic variation. Systematic functional assays of point mutants of proteins have provided valuable empirical information…
3D RNA and functional interactions from evolutionary couplings
Caleb Weinreb, Adam J. Riesselman, John B. Ingraham +3
Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence…