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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Graph Neural Networks for Physical Models of Collective Cell Migration

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Pirenne, Lize ULiège
Promotor(s) : Louppe, Gilles ULiège
Date of defense : 4-Sep-2023/5-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/18186
Details
Title : Graph Neural Networks for Physical Models of Collective Cell Migration
Author : Pirenne, Lize ULiège
Date of defense  : 4-Sep-2023/5-Sep-2023
Advisor(s) : Louppe, Gilles ULiège
Committee's member(s) : Marée, Raphaël ULiège
Huynh-Thu, Vân Anh ULiège
Stillman, Namid 
Language : English
Number of pages : 81
Keywords : [en] GNN
[en] graph neural networks
[en] cell migration
[en] simulator
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] In this thesis, a graph neural network generates likely distributions of
the set of points corresponding to the positions of cells after a discrete time
step from a particular initial disposition. The predictions of this probabilistic program will be sampled to be used over and over again to simulate
the trajectories of each cell. From this generated set of trajectories, summary statistics that reflect the expected behavior will be compared against
the same statistics computed on 2D synthetic data generated from a cell
migration simulator and then real, computer annotated, data coming from
roaming neural crest cells in a dish. Finally, the mechanics of the model will
be analyzed in order to collect an understanding of its decision processes,
which will be compared to the known mechanics of the simulator.
It will be shown that it is possible to train a small and scalable model
to produce accurate trajectories for most of the scenarios studied and that,
with only a few real, computer annotated, samples, this model still offered
interesting inference capabilities. The explainability of graph neural networks and attention layers will be leveraged to offer some insight on the
decision processes.
The use of such models can contribute to improving automatic data
annotation and provide alternate angles to study the mechanisms of cell
migration.


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Document(s)

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Access Thesis.pdf
Description: The thesis along with its bibliography and appendix.
Size: 13.53 MB
Format: Adobe PDF

Annexe(s)

File
Access Architecture.png
Description: Architecture of the synthetic model used.
Size: 103.49 kB
Format: image/png
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Access hthv_vel.png
Description: Best performances on a synthetic data set.
Size: 153.47 kB
Format: image/png
File
Access htlv_mov.png
Description: Evolution of cells : predicted vs synthetic data set ground truth.
Size: 126.09 kB
Format: image/png
File
Access att.png
Description: Analysis of attention weights and distance.
Size: 169.72 kB
Format: image/png
File
Access input.png
Description: Analysis of input importance.
Size: 34.22 kB
Format: image/png
File
Access real_data.png
Description: Performances on real data.
Size: 198.08 kB
Format: image/png
File
Access Preface.pdf
Description: Abstract of the thesis.
Size: 62.01 kB
Format: Adobe PDF

Author

  • Pirenne, Lize ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Marée, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Huynh-Thu, Vân Anh ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Stillman, Namid
  • Total number of views 14
  • Total number of downloads 41










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