通过在逆伊辛问题中集成异构数据来改进景观推断
Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
摘要：
Ising逆问题及其推广到Potts模型和连续自旋模型的研究，由于在生物学数据统计建模中的成功应用，近年来引起了人们的广泛关注。在标准设置中，伊辛模型的参数（耦合和场）是使用从玻尔兹曼分布中提取的平衡配置的样本来推断的。然而，在生物应用的背景下，数量有限的微观自旋配置的定量信息最近已经变得可用。在本文中，我们扩展了通常的逆伊辛模型的设置开发一个综合的方法相结合的平衡样本（可能有噪声）的测量的能量进行了一些任意的配置。使用模拟数据，我们表明，我们的综合方法优于标准的推断，仅基于平衡样本或能量测量，包括噪声能量测量的误差校正。作为一个生物学的概念验证应用程序，我们表明，蛋白质中的突变适应度景观可以更好地描述时，结合进化序列数据与突变序列的互补结构信息。
英文摘要：
The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are inferred using a sample of equilibrium configurations drawn from the Boltzmann distribution. However, in the context of biological applications, quantitative information for a limited number of microscopic spins configurations has recently become available. In this paper, we extend the usual setting of the inverse Ising model by developing an integrative approach combining the equilibrium sample with (possibly noisy) measurements of the energy performed for a number of arbitrary configurations. Using simulated data, we show that our integrative approach outperforms standard inference based only on the equilibrium sample or the energy measurements, including error correction of noisy energy measurements. As a biological proof-of-concept application, we show that mutational fitness landscapes in proteins can be better described when combining evolutionary sequence data with complementary structural information about mutant sequences.
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