2008/11/06 by Yasser Roudi, Sheila Nirenberg, Peter E. Latham +1 · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Physics and Astronomy · #Artificial intelligence #Computer science #Crossover #Entropy (arrow of time) #Machine learning #Mathematics #Neural Networks and Applications #Neural dynamics and brain function #Pairwise comparison #Point (geometry) #Predictive power #Protein Structure and Dynamics #cond-mat.dis-nn #q-bio.NC #q-bio.QM
paper · pdf · doi:10.1371/journal.pcbi.1000380
arxiv created 2008/11/06 · openalex publication_date 2009/05/07 · arxiv updated 2015/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point.