Inverse inference in the asymmetric Ising model
type: Dataset
description: Recent experimental techniques in biology made possible the acquisition of overwhelming amounts of data concerning complex biological networks, such as neural networks, gene regulation networks and protein-protein interaction networks. These techniques are able to record states of individual components of such networks (neurons, genes, proteins) for a large number of configurations. However, the most biologically relevantinformation lies in their connectivity and in the way their components interact, information that these techniques aren't able to record directly. The aim of this thesis is to study statistical methods for inferring information about the connectivity of complex networks starting from experimental data. The subject is approached from a statistical physics point of view drawing from the arsenal of methods developed in the study of spin glasses. Spin-glasses are prototypes of networks of discrete variables interacting in a complex way and are widely used to model biological networks. After an introduction of the models used and a discussion on the biological motivation of the thesis, all known methods of network inference are introduced and analysed from the point of view of their performance. Then, in the third part of the thesis, a new method is proposed which relies in the remark that the interactions in biology are not necessarily symmetric (i.e. the interaction from node A to node B is not the same as the one from B to A). It is shown that this assumption leads to methods that are both exact and efficient. This means that the interactions can be computed exactly, given a sufficient amount of data, and in a reasonable amount of time. This is an important original contribution since no other method is known to be both exact and efficient.
identifier: NNT: 2013PA112029
issued: 2026-05-09T11:52:31.152862
language: en
modified: 2026-05-09T11:52:31.152867
contact point: Organization
  fn: CCSD
keyword: asymmetric-interactions
keyword: biological-networks
keyword: complex-networks
keyword: disordered-systems
keyword: gene-regulatory-networks
keyword: graphical-models
keyword: inference-statistique
keyword: infoeu-reposemanticsdoctoralthesis
keyword: information-theory
keyword: interactions-asymetriques
keyword: inverse-ising-problem
keyword: kinetic-ising-model
keyword: modele-dising-cinetique
keyword: modeles-graphiques
keyword: neural-networks
keyword: physcondcm-genphysics-physicscondensed-matter-cond-matother-cond-matother
keyword: physique-statistique
keyword: probleme-dising-inverse
keyword: reseaux-biologiques
keyword: reseaux-complexes
keyword: reseaux-de-neurones
keyword: reseaux-de-regulation-de-genes
keyword: spin-glasses
keyword: statistical-inference
keyword: statistical-physics
keyword: systemes-desordonnes
keyword: theorie-de-linformation
keyword: theses
keyword: verres-de-spin
landing page: https://theses.hal.science/tel-00869738

Inverse inference in the asymmetric Ising model
type: Distribution
format: HTML
issued: 2026-05-09T11:52:31.197049
modified: 2026-05-09T11:52:31.118447
access URL: https://theses.hal.science/tel-00869738

test_moissonnage_selune
type: Agent