English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The inverse Ising problem and its generalizations to Potts and continuousspin models have recently attracted much attention thanks to their successfulapplications in the statistical modeling of biological data. In the standardsetting, the parameters of an Ising model (couplings and fields) are inferredusing a sample of equilibrium configurations drawn from the Boltzmanndistribution. However, in the context of biological applications, quantitativeinformation for a limited number of microscopic spins configurations hasrecently become available. In this paper, we extend the usual setting of theinverse Ising model by developing an integrative approach combining theequilibrium sample with (possibly noisy) measurements of the energy performedfor a number of arbitrary configurations. Using simulated data, we show thatour integrative approach outperforms standard inference based only on theequilibrium sample or the energy measurements, including error correction ofnoisy energy measurements. As a biological proof-of-concept application, weshow that mutational fitness landscapes in proteins can be better describedwhen combining evolutionary sequence data with complementary structuralinformation about mutant sequences.

Improving landscape inference by integrating heterogeneous data in the inverse Ising problem