activity
20182022
most citedVariational auto-encoding of protein sequences

49 citations · 88 across the 3 of their papers we have counts for

collaborators

5 papers

q-bio.QM2022

Forecasting labels under distribution-shift for machine-guided sequence design

Lauren Berk Wheelock, Stephen Malina, Jeffrey Gerold +1

The ability to design and optimize biological sequences with specific functionalities would unlock enormous value in technology and healthcare. In recent years, machine learning-gu…

q-bio.QM2020

A primer on model-guided exploration of fitness landscapes for biological sequence design

Sam Sinai, Eric D Kelsic

Machine learning methods are increasingly employed to address challenges faced by biologists. One area that will greatly benefit from this cross-pollination is the problem of biolo…

cs.LG202039 cited

AdaLead: A simple and robust adaptive greedy search algorithm for sequence design

Sam Sinai, Richard Wang, Alexander Whatley +3

Efficient design of biological sequences will have a great impact across many industrial and healthcare domains. However, discovering improved sequences requires solving a difficul…

q-bio.PE2019

Turbulent coherent structures and early life below the Kolmogorov scale

Madison S. Krieger, Sam Sinai, Martin A. Nowak

A great number of biological organisms live in aqueous environments. Major evolutionary transitions, including the emergence of life itself, likely occurred in such environments. W…

q-bio.QM201849 cited

Variational auto-encoding of protein sequences

Sam Sinai, Eric Kelsic, George M. Church +1

Proteins are responsible for the most diverse set of functions in biology. The ability to extract information from protein sequences and to predict the effects of mutations is extr…