--- _id: '11638' abstract: - lang: eng text: 'Statistical inference is central to many scientific endeavors, yet how it works remains unresolved. Answering this requires a quantitative understanding of the intrinsic interplay between statistical models, inference methods, and the structure in the data. To this end, we characterize the efficacy of direct coupling analysis (DCA)—a highly successful method for analyzing amino acid sequence data—in inferring pairwise interactions from samples of ferromagnetic Ising models on random graphs. Our approach allows for physically motivated exploration of qualitatively distinct data regimes separated by phase transitions. We show that inference quality depends strongly on the nature of data-generating distributions: optimal accuracy occurs at an intermediate temperature where the detrimental effects from macroscopic order and thermal noise are minimal. Importantly our results indicate that DCA does not always outperform its local-statistics-based predecessors; while DCA excels at low temperatures, it becomes inferior to simple correlation thresholding at virtually all temperatures when data are limited. Our findings offer insights into the regime in which DCA operates so successfully, and more broadly, how inference interacts with the structure in the data.' acknowledgement: This work was supported in part by the Alfred P. Sloan Foundation, the Simons Foundation, the National Institutes of Health under Award No. R01EB026943, and the National Science Foundation, through the Center for the Physics of Biological Function (PHY-1734030). article_number: '023240' article_processing_charge: No article_type: original author: - first_name: Vudtiwat full_name: Ngampruetikorn, Vudtiwat last_name: Ngampruetikorn - first_name: Vedant full_name: Sachdeva, Vedant last_name: Sachdeva - first_name: Johanna full_name: Torrence, Johanna last_name: Torrence - first_name: Jan full_name: Humplik, Jan id: 2E9627A8-F248-11E8-B48F-1D18A9856A87 last_name: Humplik - first_name: David J. full_name: Schwab, David J. last_name: Schwab - first_name: Stephanie E. full_name: Palmer, Stephanie E. last_name: Palmer citation: ama: Ngampruetikorn V, Sachdeva V, Torrence J, Humplik J, Schwab DJ, Palmer SE. Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. 2022;4(2). doi:10.1103/PhysRevResearch.4.023240 apa: Ngampruetikorn, V., Sachdeva, V., Torrence, J., Humplik, J., Schwab, D. J., & Palmer, S. E. (2022). Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. American Physical Society. https://doi.org/10.1103/PhysRevResearch.4.023240 chicago: Ngampruetikorn, Vudtiwat, Vedant Sachdeva, Johanna Torrence, Jan Humplik, David J. Schwab, and Stephanie E. Palmer. “Inferring Couplings in Networks across Order-Disorder Phase Transitions.” Physical Review Research. American Physical Society, 2022. https://doi.org/10.1103/PhysRevResearch.4.023240. ieee: V. Ngampruetikorn, V. Sachdeva, J. Torrence, J. Humplik, D. J. Schwab, and S. E. Palmer, “Inferring couplings in networks across order-disorder phase transitions,” Physical Review Research, vol. 4, no. 2. American Physical Society, 2022. ista: Ngampruetikorn V, Sachdeva V, Torrence J, Humplik J, Schwab DJ, Palmer SE. 2022. Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. 4(2), 023240. mla: Ngampruetikorn, Vudtiwat, et al. “Inferring Couplings in Networks across Order-Disorder Phase Transitions.” Physical Review Research, vol. 4, no. 2, 023240, American Physical Society, 2022, doi:10.1103/PhysRevResearch.4.023240. short: V. Ngampruetikorn, V. Sachdeva, J. Torrence, J. Humplik, D.J. Schwab, S.E. Palmer, Physical Review Research 4 (2022). date_created: 2022-07-24T22:01:42Z date_published: 2022-06-24T00:00:00Z date_updated: 2022-07-25T07:52:35Z day: '24' ddc: - '530' department: - _id: GaTk doi: 10.1103/PhysRevResearch.4.023240 external_id: arxiv: - '2106.02349' file: - access_level: open_access checksum: ed6fdc2a3a096df785fa5f7b17b716c6 content_type: application/pdf creator: dernst date_created: 2022-07-25T07:47:23Z date_updated: 2022-07-25T07:47:23Z file_id: '11644' file_name: 2022_PhysicalReviewResearch_Ngampruetikorn.pdf file_size: 1379683 relation: main_file success: 1 file_date_updated: 2022-07-25T07:47:23Z funded_apc: '1' has_accepted_license: '1' intvolume: ' 4' issue: '2' language: - iso: eng month: '06' oa: 1 oa_version: Published Version publication: Physical Review Research publication_identifier: issn: - 2643-1564 publication_status: published publisher: American Physical Society quality_controlled: '1' scopus_import: '1' status: public title: Inferring couplings in networks across order-disorder phase transitions tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 4 year: '2022' ... --- _id: '720' abstract: - lang: eng text: 'Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.' article_number: e1005763 article_processing_charge: Yes author: - first_name: Jan full_name: Humplik, Jan id: 2E9627A8-F248-11E8-B48F-1D18A9856A87 last_name: Humplik - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 2017;13(9). doi:10.1371/journal.pcbi.1005763 apa: Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763 chicago: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763. ieee: J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” PLoS Computational Biology, vol. 13, no. 9. Public Library of Science, 2017. ista: Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763. mla: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763. short: J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017). date_created: 2018-12-11T11:48:08Z date_published: 2017-09-19T00:00:00Z date_updated: 2021-01-12T08:12:21Z day: '19' ddc: - '530' - '571' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005763 file: - access_level: open_access checksum: 81107096c19771c36ddbe6f0282a3acb content_type: application/pdf creator: system date_created: 2018-12-12T10:18:30Z date_updated: 2020-07-14T12:47:53Z file_id: '5352' file_name: IST-2017-884-v1+1_journal.pcbi.1005763.pdf file_size: 14167050 relation: main_file file_date_updated: 2020-07-14T12:47:53Z has_accepted_license: '1' intvolume: ' 13' issue: '9' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 255008E4-B435-11E9-9278-68D0E5697425 grant_number: RGP0065/2012 name: Information processing and computation in fish groups - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '6960' pubrep_id: '884' quality_controlled: '1' scopus_import: 1 status: public title: Probabilistic models for neural populations that naturally capture global coupling and criticality tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 13 year: '2017' ... --- _id: '1928' abstract: - lang: eng text: In infectious disease epidemiology the basic reproductive ratio, R0, is defined as the average number of new infections caused by a single infected individual in a fully susceptible population. Many models describing competition for hosts between non-interacting pathogen strains in an infinite population lead to the conclusion that selection favors invasion of new strains if and only if they have higher R0 values than the resident. Here we demonstrate that this picture fails in finite populations. Using a simple stochastic SIS model, we show that in general there is no analogous optimization principle. We find that successive invasions may in some cases lead to strains that infect a smaller fraction of the host population, and that mutually invasible pathogen strains exist. In the limit of weak selection we demonstrate that an optimization principle does exist, although it differs from R0 maximization. For strains with very large R0, we derive an expression for this local fitness function and use it to establish a lower bound for the error caused by neglecting stochastic effects. Furthermore, we apply this weak selection limit to investigate the selection dynamics in the presence of a trade-off between the virulence and the transmission rate of a pathogen. acknowledgement: J.H. received support from the Zdenek Bakala Foundation and the Mobility Fund of Charles University in Prague. author: - first_name: Jan full_name: Humplik, Jan id: 2E9627A8-F248-11E8-B48F-1D18A9856A87 last_name: Humplik - first_name: Alison full_name: Hill, Alison last_name: Hill - first_name: Martin full_name: Nowak, Martin last_name: Nowak citation: ama: Humplik J, Hill A, Nowak M. Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. 2014;360:149-162. doi:10.1016/j.jtbi.2014.06.039 apa: Humplik, J., Hill, A., & Nowak, M. (2014). Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. Elsevier. https://doi.org/10.1016/j.jtbi.2014.06.039 chicago: Humplik, Jan, Alison Hill, and Martin Nowak. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” Journal of Theoretical Biology. Elsevier, 2014. https://doi.org/10.1016/j.jtbi.2014.06.039. ieee: J. Humplik, A. Hill, and M. Nowak, “Evolutionary dynamics of infectious diseases in finite populations,” Journal of Theoretical Biology, vol. 360. Elsevier, pp. 149–162, 2014. ista: Humplik J, Hill A, Nowak M. 2014. Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. 360, 149–162. mla: Humplik, Jan, et al. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” Journal of Theoretical Biology, vol. 360, Elsevier, 2014, pp. 149–62, doi:10.1016/j.jtbi.2014.06.039. short: J. Humplik, A. Hill, M. Nowak, Journal of Theoretical Biology 360 (2014) 149–162. date_created: 2018-12-11T11:54:46Z date_published: 2014-11-07T00:00:00Z date_updated: 2021-01-12T06:54:08Z day: '07' department: - _id: GaTk doi: 10.1016/j.jtbi.2014.06.039 intvolume: ' 360' language: - iso: eng month: '11' oa_version: None page: 149 - 162 publication: Journal of Theoretical Biology publication_status: published publisher: Elsevier publist_id: '5166' scopus_import: 1 status: public title: Evolutionary dynamics of infectious diseases in finite populations type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 360 year: '2014' ...