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Laurent Duval, research amateur in data science, signal analysis, image processing, machine learning; aside: sparsity, puns and anagrams
Motivation: The cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. With increasing omics technologies, large-scale disparate data is generated. It has increased the use of molecular and functional interaction networks: gene co-expression, protein�protein interaction
(PPI), genetic interaction, and metabolic networks. They are rich sources of information at different
molecular levels. Effective integration of this biological information is essential to understand cell functioning
and their building blocks (proteins). Therefore, it is necessary to obtain informative representations of
proteins and their proximity that is not fully captured by the features extracted directly from one level of
information. We propose BraneMF, a novel random walk-based matrix factorization method for learning
node representation in multilayer networks with application to omics data integration.
Results: We test BraneMF with PPI networks of Saccharomyces cerevisiae, a well-studied yeast model
organism. We demonstrate the applicability of learned features for essential multi-omics inference tasks:
clustering, function and PPI prediction. We compare it to state-of-the-art integration methods for multilayer
networks. BraneMF outperforms baseline methods by achieving high prediction scores for a variety of
downstream tasks. The robustness of results is assessed by an extensive parameter sensitivity analysis.
Availability: BraneMF is freely available at: https://github.com/Surabhivj/BraneMF
Background: Gene expression is regulated at different molecular levels, including
chromatin accessibility, transcription, RNA maturation, and transport. These
regulatory mechanisms have strong connections with cellular metabolism. In order
to study the cellular system and its functioning, omics data at each molecular
level can be generated and efficiently integrated. Here, we propose BRANEnet,
a novel multi-omics integration framework for multilayer heterogeneous networks.
BRANEnet is an expressive, scalable, and versatile method to learn node
embeddings, leveraging random walk information within a matrix factorization
framework. Our goal is to efficiently integrate multi-omics data to study different
regulatory aspects of multilayered processes that occur in organisms. We evaluate
our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied
yeast model organism.
Results: We test BRANEnet on transcriptomics (RNA-seq) and targeted
metabolomics (NMR) data for wild-type yeast strain during a heat-shock time
course of 0, 20, and 120 minutes. Our framework learns features for differentially
expressed bio-molecules showing heat stress response. We demonstrate the
applicability of the learned features for targeted omics inference tasks:
transcription factor (TF)-target prediction, integrated omics network (ION)
inference, and module identification. The performance of BRANEnet is
compared with existing network integration methods. Our model outperforms
baseline methods by achieving high prediction scores for a variety of downstream
tasks
Matlab SPOQ sparse restoration toolbox: SPOQ lp-Over-lq Regularization for Sparse Signal Recovery applied to Mass Spectrometry.
IEEE Transactions on Signal Processing, 2020 [opus] [code] [matlabcentral] [doi]
Lauriane BOUARD will defend his PhD thesis on 1st March 2021 at Université Côte d'Azur (Nice) on
"Refinable resolution and precision for volume mesh compression and simulation in geosciences"
IFPEN's Scientific Board awarded the 2018 Yves Chauvin prize to Aurélie Pirayre for her thesis entitled "Reconstruction and Clustering with Graph optimization and Priors on Gene networks and Images". Aurélie Pirayre received her award at the ceremony held at IFPEN's Rueil-Malmaison site on 19 November 2018.
Her work represents an advance in the adaptation of image processing methods for the purposes of representing biological data in graph form. Directed by Jean-Christophe Pesquet (Université Paris-Est Marne-la-Vallée, now CentraleSupélec), her research was supervised by Frédérique Bidard-Michelot and Laurent Duval at IFPEN.
Lauriane BOUARD, starting November 2017: Progressive compression of large evolutionary volumic meshes: geometry and properties (Compression progressive de maillages volumiques massifs et évolutifs : géométrie et propriétés), supervised with Frédéric Payan, Marc Antonini (MediaCoding Laboratory, Université Côte d'Azur)
Arthur MARMIN, CentraleSupélec PhD grant (formely Carnot Energie), starting Oct. 2017: Modèles rationnels optimisés de manière exacte pour l'amélioration des procédés chimiques (Exactly optimized rational models for chemical process improvement), coordinated with Marc Castella (TelecomParisSud) and Jean-Christophe Pesquet (CentraleSupélec)
Talk: two talks on adaptive filtering in wavelet frames ("Morphing adaptatif de modèles en trames d'ondelettes : application au filtrage de multiples en sismique "), and on directional/geometric wavelet transforms form image processing ("Petite histoire en images des transformées multi-échelle et en ondelettes en deux-dimensions (curvelets, contourlets, shearlets, dual-tree, etc.)") at Séminaire de Vannes du LMBA