---
_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
arxiv: 1
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'
...