Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : IBISC – Informatique, BioInformatique, Systèmes Co
Durée : 3 ans
Contact : Khalifa.Djemal@ibisc.univ-evry.fr
Date limite de publication : 2021-05-16

Contexte :
Depuis quelques années, différents travaux de recherche scientifique ont démontrés que la qualité de l’air a un impact sur la santé et devient un sujet de plus en plus préoccupant à l’échelle urbaine. L’identification et la géolocalisation de sources de pollution atmosphérique est donc un enjeu important et repose sur l’utilisation d’un grand nombre de capteurs de gaz multimodaux fixes et/ou embarqués.
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In recent years, various scientific research studies have shown that air quality has an impact on health and is becoming an increasingly important issue at the urban area. Identification and geolocation of air pollution sources are therefore an important issue and rely on the use of a large number of fixed and/or on-board multimodal gas sensors.

Sujet :
En recherche scientifique, l’identification de sources polluantes repose sur la résolution d’un modèle inverse complexe mal posé au regard des données observées. La dispersion de polluants est généralement surveillée par des capteurs placés dans un domaine spatialement discret et fournissent des observations temporelles. Ces observations sont ensuite utilisées pour estimer les propriétés des sources de contaminants, par exemple leurs positions, leurs débits de rejet dans l’atmosphère et les paramètres du modèle régissant la dispersion de ces contaminants (par exemple la dispersion, la topographie du site, la météorologie, etc.). Ces estimations sont essentielles pour une évaluation fiable des dangers et des risques de contamination. Dans le cas particulier de plusieurs sources de contamination (avec des positions et des débits d’émission différents), les observations représentent un mélange ou une combinaison de deux ou plusieurs polluants.
Dans ce cadre, le travail attendu consistera en la résolution d’un problème de localisation de sources polluantes en environnement de type urbain avec un réseau de capteurs fixes et/ou mobiles. En effet, à partir de données optimisées, issues de campagnes de mesures existantes, c’est-à-dire des sources identifiées et localisées dans un environnement connu, il s’agira dans un premier temps, de mettre en œuvre un modèle d’apprentissage profond avec la prise en compte de manière active des différents paramètres des capteurs. Dans un second temps, le modèle construit avec une stratégie d’apprentissage actif, sera ensuite capable d’identifier et de donner une estimation de la position des sources polluantes dans un environnement inconnu.
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In scientific research, the identification of pollution emission sources is based on the resolution of a complex inverse model that is ill-posed with respect to the observed data. Pollutant dispersion is generally monitored by sensors placed in a spatially discrete domain and provide temporal observations. These observations are then used to estimate the properties of contaminant sources, such as their positions, atmospheric release rates and the model parameters governing the dispersion of these contaminants (e.g. velocity, dispersivity, site topography, meteorology, etc.). These estimations are essential for a reliable assessment of the hazards and risks of contamination. In the particular case of several sources of contamination (with different positions and release rates), the observations represent a mixture or combination of two or more pollutants.
In this framework, the expected work will consist of solving a problem of multiple sources localization in urban/industrial environments with a network of fixed and/or mobile sensors. Indeed, using optimized data from existing measurement campaigns, i.e. sources identified and located in a known environment, this project will initially consist of implementing a deep learning model. In a second step, the model thus built, with an active learning strategy, will then be able to identify and give in an unknown environment, an estimation of the position and intensity of the emission sources.

Profil du candidat :
De niveau Master2 recherche ou équivalent, en Intelligence Artificielle (IA) et informatique ou Mathématiques appliquées (modélisation et calculs scientifiques).
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Master 2 research or equivalent, in Artificial Intelligence (AI) and Computer Science.

Formation et compétences requises :
La maîtrise des méthodes et des outils de traitement et analyse de base de données, des langages Python et C, sont vivement souhaités. Des connaissances de base en sciences de l’environnement atmosphérique seront également très appréciées.
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Knowledge of data processing methods and tools,
languages such as Python and C, is highly desirable. Basic knowledge of atmospheric environmental sciences will also be highly
appreciated

Adresse d’emploi :
IBISC -Université d’Evry Val d’Essonne
40 rue du Pelvoux
91000 Evry.

Vous pouvez candidater directement sur la plateforme ADUM:
https://www.adum.fr

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LAMSADE
Durée : 3 years
Contact : alexandre.allauzen@dauphine.psl.eu
Date limite de publication : 2021-09-03

Contexte :
This is a 3-year PhD position, funded by Foxstream, a software company
(since 2004), specialized in real-time automated processing of video
content analysis. The PhD thesis is a collaboration with Dauphine
Université (the MILES team of the LAMSADE) with a join supervision
(Quentin Barthélemy from Foxstream and Alexandre Allauzen from MILES).
The PhD student will be located at Paris-Dauphine University in close
relationships with Foxstream.

Sujet :
For a couple of decades, Deep Learning (DL) added a huge boost to the
already rapidly developing field of computer vision. While for some
kind of data and tasks, DL is the most successful approach, this is
not the case for all applications. For instance, the analysis of video
streams generated by thermal cameras is still a research challenge
because of the long range perimeter, the depth of focus and the
associated geometrical issues, along with the frequent calibration
change. Therefore, the stability and robustness of DL models must be
better characterized and improved.

Very recently, Transformer architectures have achieved state of the
art performances in many domains: from natural language processing to
computer vision. In this thesis we will explore the use of Tranformers
for videos generated by thermal cameras and their properties.

From a theoritical and application perspectives, the goals are to
explore the stability of such architectures, the robustness against
adversarial examples, and what kind of invariances and symetries can
be captured.

Profil du candidat :
– Outstanding master’s degree (or an equivalent university degree) in
computer science or another related disciplines (as e.g. mathematics,
information sciences, computer engineering, etc.).
– Proficiency in machine learning, computer vision, or signal
processing.
– Fluency in spoken and written English is required.

Formation et compétences requises :
Application:
To apply, please email alexandre.allauzen [at] dauphine.psl.eu with:
– a curriculum vitae, with contact of 2 or more referees
– a cover letter
– a research outcome (e.g. master thesis and/or published papers) of
the candidate
– a transcript of grades

Adresse d’emploi :
Université Paris Dauphine

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : LAMSADE
Durée : 3 years
Contact : alexandre.allauzen@dauphine.psl.eu
Date limite de publication : 2021-09-03

Contexte :
We invite applications for a fully-funded PhD position on the topic of
“Deep Learning for physical systems modelling”.

This is a 3-year position funded by the ANR project SPEED and it will
start next fall (as soon as possible). The whole project is a
collaboration between the IJLRA, the LAMSADE and the new LISN labs.
Frequent scientific discussions and meetings are planed with many
students working in this project.

The interaction between machine learning and Physics has recently
emerged as a new and important research area. Some illustrations are
simulations of complex physical systems with machine learning models,
or at the opposite, the introduction of numerical methods in machine
learning.

Sujet :
At the interfaces of AI and Physics, different tracks can be
explored depending on the skills of the candidate:

– Noisy, scarce and partial observations of physical systems.
– Training algorithm to enforce physical properties.
– Interaction between the machine learning model and the physical
systems
– Dealing with chaoticity

The algorithm developments should be assessed in interesting physical
situations. The accent is put here on chaotic dynamical systems as a
paradigm of complex systems. In particular, leaving aside the pure
data-driven approach, it would be important to find out whether a
physical-informed approach can overcome some of the challenges raised
by a blind use of machine-learning.

Profil du candidat :
– Outstanding master’s degree (or an equivalent university degree) in
computer science or Physics and other related disciplines (as e.g. mathematics, information sciences, computer engineering, etc.).
– Proficiency in machine learning and data analysis

Formation et compétences requises :
– Fluency in spoken and written English is required.
– The knowledge of python and pytorch is welcome

Adresse d’emploi :
Dauphine Université

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Laboratoire d’Informatique de Paris 6
Durée : 36 mois
Contact : patrick.gallinari@sorbonne-universite.fr
Date limite de publication : 2021-09-30

Contexte :
Deep Learning has found important success in many application fields. It is beginning to be explored for scientific computing in domains traditionally dominated by physics models (first principles) like earth science, climate science, biological science, etc. It is particularly promising in problems involving processes that are not completely understood, or computationally too complex to solve by running the physics inspired model. However, the application of state of the art DNN models often meets limited success in scientific applications. This is due to different factors: the complexity of the underlying physical phenomenon, the large data requirement of deep neural networks (DNNs), their inability to produce physically consistent results. The research community has started to explore how to integrate physics knowledge and data, a challenging direction. We consider here the modeling of complex dynamical systems characterizing natural phenomena with a focus on climate modeling applications, and with the objective of combining model based physics (MB) and machine learning (ML) approaches.

Sujet :
Research directions

Combining Physics and Deep Learning

The integration of physics background and ML has recently motivated the interest of several communities (Willard 2020). This issue may been explored from different perspectives. We will focus here on the modeling of spatio-temporal dynamics such as those underlying earth science and climate observations. The classical modeling tools for such dynamics in physics and applied mathematics rely on partial differential equations (PDE). We then consider situations where the physical prior background is provided by PDEs. We are interested in solving two different problems. A first problem corresponds to the situation where the PDEs are too complex to run a full simulation and ones wants to reduce the simulation cost. A possible strategy here is to run a simulation at a coarse precision and use ML for complementing the physical simulation and reach high fidelity prediction. The second problem corresponds to the case where the PDE only provides partial information about the underlying physical phenomena and this physical knowledge it is to be complemented with ML by extracting the complementary information from data. Although they correspond to different objectives, the two problems share many similarities from a ML point of view. Initial attempts to solve similar problems can be found in recent work such as (de Bezenac 2018, Harlim 2020, Yin 2021). This will be further developed during the PhD project with the objective of analyzing and developing different integration frameworks.

Learning at Multiple Scales

Modeling dynamical physical processes often requires solving PDEs at different spatio-temporal scales. For example in climate, global phenomena are influenced by dynamics operating at a smaller scale. Global simulation models could not be run, due to their complexity, at fine discretization levels. This problem is known as “downscaling” and DNNs could help improve this multiscale problem. Similar problems occur e.g. in computational fluid dynamics. Learning at different scales is an open issue in ML. Most current DNN deployments for learning dynamics operate at a fixed spatio-temporal discretization. Recent advances (Sitzman 2020, Li 2021) allow us learning a function space instead of discrete flows and open the possibility for generalizing at different spatio-temporal resolutions. This will be used as starting point for learning at different scales with DNNs.

Uncertainty Quantification

Uncertainty quantification is of great importance in climate modeling. This requires characterizing the distribution p(y|x) where y is the response and x the covariates of interest. Since Monte Carlo simulations are unfeasible for such applications, physics has developed solutions such as reduced order models for modeling uncertainty while ML often relies on Gaussian Processes for quantifying uncertainty in physical processes. However none of these approaches scales well to high dimensions. We will explore recent developments based on Neural Processes (Garnelo 2018, Norcliffe 2021) for modeling uncertainty.

References

Ayed, I., de Bezenac, Emmanuel , Pajot, A., Brajard, J. and Gallinari, P. 2019. Learning the hidden dynamics of ocean temperature with Neural Networks. Climate Informatics (2019).
de Bezenac, E., Pajot, A. and Gallinari, P. 2018. Deep Learning For Physical Processes: Incorporating Prior Scientific Knowledge. ICLR (2018).
Garnelo, M., Rosenbaum, D., Maddison, C.J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y.W., Rezende, D.J. and Ali Eslami, S.M. 2018. Conditional neural processes. ICML (2018), 1704–1713.
Harlim, J., Jiang, S.W., Liang, S. and Yang, H. 2021. Machine learning for prediction with missing dynamics. Journal of Computational Physics. 428, (2021), 109922.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A. and Anandkumar, A. Fourier Neural Operator for Parametric Partial Differential Equations. ICLR (2021), 1–16.
Norcliffe, A., Cristian, B., Day, B., Moss, J. and Liò, P. 2021. Neural ODE Processes. ICLR (2021), 1–14.
Sitzmann, V., Martel, J.N.P., Bergman, A.W., Lindell, D.B., Wetzstein, G. and University, S. 2020. Implicit Neural Representations with Periodic Activation Functions. Neurips (2020).
Willard, J.D., Jia, X., Xu, S., Steinbach, M. and Kumar, V. 2020. Integrating physics-based modeling with machine learning: A survey. arXiv (2020), 1–34.
Yin, Y., Le Guen, V., Dona, J., de Bezenac, E., Ayed, I., Thome, N. and Gallinari, P. 2021. Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. ICLR (2021).

Profil du candidat :
Master in computer science or applied mathematics, Engineering school.

Formation et compétences requises :
Strong background and experience in machine learning and good technical skills in programming.

Adresse d’emploi :
Sorbonne Université, Pierre et Marie Curie Campus, 4 Place Jussieu, Paris, Fr

Document attaché : 202105061626_Integrating Deep Learning and Physics for Modeling Complex Dynamics, Applications to Climate Science.pdf

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : Inria Paris et Centre de Recherche des Cordeliers
Durée : 3 ans
Contact : adrien.coulet@inria.fr
Date limite de publication : 2021-05-31

Contexte :
Nous cherchons le bon candidat pour une thèse en informatique avec de l’apprentissage, de la représentation de connaissances, et leurs applications en santé.
La thèse se fera à Paris, dans l’équipe HeKA commune à Inria, Inserm et Université de Paris (https://team.inria.fr/heka/).

N’hésitez pas à transférer ce message.
Si vous êtes intéressés, contactez-nous svp (antoine.neuraz@aphp.fr, adrien.coulet@inria.fr), si possible avant le 18 mai.

Sujet :
Le titre de la thèse : “Apprentissage de processus de décision diagnostique: Expérimentations pour le diagnostic des affections fréquentes à partir de Dossier Patients Informatisés”
Le sujet : https://team.inria.fr/heka/files/2021/05/phd_subject_learn_diagnostic_processes.pdf
Plus d’infos : https://jobs.inria.fr/public/classic/fr/offres/2021-03675

Profil du candidat :
Plusieurs profils sont possibles

Formation et compétences requises :
M2 en informatique ou mathématique appliquées ou santé publique ou bioinformatique

Adresse d’emploi :
Paris

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Laboratoire IBISC, Université d’Evry / Université
Durée : 3 ans
Contact : fariza.tahi@univ-evry.fr
Date limite de publication : 2021-05-17

Contexte :
Développement de méthodes computationnelles pour l’étude des ARN non-codants impliqués dans le cancer de vessie
In recent years, machine learning methods, particularly deep learning, have grown considerably, and have shown their effectiveness in a large number of fields, including biology and medicine. More and more bioinformatic methods and tools based on deep learning are proposed in the literature, to answer various biological and biomedical questions. In this project, we want to propose new deep learning methods for the prediction and analysis of particular genomic sequences, the non-coding RNAs (ncRNAs), in a biomedical context: their involvement in bladder cancer.
Non-coding RNAs, RNAs which do not code for proteins and constitute the largest part of genomes, are increasingly identified as playing important roles in the deregulation processes leading to pathologies, such as cancer (Anastasiadou et al., 2018). They are thus considered as potential diagnostic markers and therapeutic targets. Their identification and the determination of their function are important issues, and with the next generation sequencing (NGS) which generate considerable volumes of omics data, their prediction and their characterization by in silico methods is essential to make it possible to orient the experimental studies.
Recently, long ncRNAs (lncRNAs), larger than 200 nucleotides, have been identified as potential regulators. But unlike small ncRNAs, their characterization and classification by structure and function are far from established. Determining the structure of a lncRNA is a difficult problem, both by experimental (crystallography, NMR) and bioinformatic methods. Determining its function is even more difficult, especially since unlike proteins, ncRNAs with similar functions often lack sequence homology (RNA sequences show compensatory mutations maintaining structural conservation). Attempts to classify lncRNAs have been proposed, based on different criteria: length of transcripts, location, association with genes encoding proteins. A summary of these classifications has been proposed in (St. Laurent et al., 2015). In (Kopp and Mendell, 2018), the authors suggest a study of lncRNAs according to their location, explaining that this is often linked to function. But a large majority of published works is dedicated to the study of a precise lncRNA. For instance, a recent study (Uroda et al., 2019) reveals the importance of the presence of a pseudoknot (particular motif of the secondary structure) in the mechanism of regulation of the MEG3 lncRNA in the biological pathway of p53, gene involved in many cancers.

From a computational point of view, a few methods have been proposed in the literature for the classification of well characterized ncRNAs whose structure is well known. These methods, based on supervised machine learning, often of deep Learning type, offer a model built on a dataset composed of 13 classes of small ncRNAs. We can cite RNAcon (Panwar et al., 2014) based on the model of random forests and nRC (Fiannaca et al., 2017) based on convolutional networks (CNN), where the secondary structure is used for classification; then more recently ncRDeep (Chantsalnyam et al., 2020) based on CNNs and ncRFP (Wang et al., 2020) based on recurrent neural networks (RNN), both considering only sequence characteristics. Very few methods are specifically interested in the classification of lncRNAs. For example, SEEKR (Kirk et al., 2018) uses the sequence, more precisely the profiles of k-mers, to group the transcripts which are most similar and form a functional class, using a clustering algorithm based on a Pearson correlation (unsupervised learning). LncADeep (Yang et al., 2018) uses a deep neural network (DNN) to identify interactions between lncRNAs and proteins, based on sequence and secondary structure. The tool then uses the annotation of proteins associated with a lncRNA to describe the biological functions in which it is potentially involved. Although these methods make it possible to specify the broad category of lncRNAs, they remain limited. In addition, the classes summarized in (St. Laurent et al., 2015) are not all identified by the existing tools. We believe that it might be possible to more finely classify lncRNAs by taking into account other characteristics.

In this project, we propose to develop original computational methods based on Deep Learning (DL) to predict, classify and identify the function of ncRNAs, including the lncRNAs, by integrating different characteristics: sequence, structure (especially secondary), genomic and chromosomal position, interaction with coding or non-coding genes, and genetic and epigenetic alterations. Two methodological challenges are to be considered: (i) making it possible to consider heterogeneous characteristics (multi-source approach); (ii) predicting known classes of ncRNAs while being able to predict new classes, and this by combining a supervised approach with an unsupervised approach. An important point that we also consider concerns the visualization part of the results, for a better understanding and interpretation by the user.

Sujet :
We are interested by Self-organizing maps (SOM), which are unsupervised neural network capable of grouping and visualizing large-scale data. Using an unsupervised competitive learning algorithm, this technique is able to produce a map, representing the input space, in which nearby data is located in regions close to the map. In order to represent heterogeneous sources, we will propose original multimodal approaches based on DL which would allow to merge the different data sources. Fusion can be performed using three main strategies (Ramachandram and Taylor, 2017): early fusion, joint fusion and late fusion. Early merging involves combining the input characteristics of different sources before using a single DL model. Joint fusion refers to the process of combining representations of inputs learned at the intermediate layers of different neural networks that represent modalities. Late fusion allows the decisions of several neural networks that process modalities to be combined to provide a final decision. We will be particularly interested in joint fusion for the classification of ncRNAs and the identification of their biological functions. To take into account the different heterogeneous sources, each data source will be processed by an adequate DL model, such as “Convolutional Neural Neworks” (CNNs), “Graph Neural Networks” (GNNs) and multi-layer perceptrons (MLPs), which will allow better extraction of high level features from this source. To allow the discovery of new classes, we will study the association of different rejection options (Geifman and El-Yaniv, 2019) to the multimodal model. The combination of this model with SOMs (Platon et al. 2018) will allow the visualization of new classes of ncRNAs. We will also be interested in identifying the data sources and the characteristics that led to the predictions (Platon et al. 2018bis). This will make it possible to explain the predictions and to discover new properties that could be associated with ncRNAs.
Résultats attendus
Application, objectives and interest in cancerology research
The deregulation of ncRNAs may participate in tumor progression but the ncRNAs involved and their roles remain poorly defined. Clinically, ncRNAs can be diagnostic markers and therapeutic targets (Roberts et al., 2020).
Cancer in a given tissue is a heterogeneous disease composed of several subtypes, each subtype being defined by a specific transcriptional program. Genetic and epigenetic alterations as well as genes involved in cancer must be studied taking into-account these subtypes. In this project we will focus on bladder cancers (Tran et al., 2021) and in particular on the papillary luminal subtype and of the basal subtype bladder cancers for which the partner team has been major contributor. The luminal papillary subtype cancers are well differentiated and present, in the majority of cases, activating genetic alterations of the gene coding for the receptor tyrosine kinase FGFR3 and activation of the nuclear receptor PPARG (Biton et al., 2014; Mahé et al., 2018; Rochel et al., 2019; Shi et al. 2020). The basal subtype is a particularly aggressive subtype (most deaths will occur within one year after diagnosis), poorly differentiated, and found not only in bladder cancers but in many other carcinomas (breast, pancreatic, lung cancers for example) (Rebouissou et al., 2014; Kamoun et al., 2019).
The goals in the project are:
1) To systematically identify and classify the ncRNAs present in tumors of the papillary luminal and basal subtypes and compare them to the ncRNAs present in normal urothelium in different physiological states: different stages of differentiation, development or healing. This will allow us to accurately compare tumor cells to normal cells.
2) To identify deregulated ncRNAs in tumor cells compared to normal cells.
3) To determine the genetic or epigenetic mechanisms of their deregulation.
4) To predict the biological functions in which these ncRNAs are involved and their roles (target genes in the case of small ncRNAs, sponge function, participation in complexes and/or regulation of transcription in the case of long ncRNAs).
For this purpose, we will use the computational tools that will be developed in this project as well as other RNA bioinformatics tools developed in the AROBAS team (and available on the EvryRNA platform (http://EvryRNA.ibisc.univ-evry.fr), such as RNANet (Becquey et al., 2020), Biorseo (Becquey et al., 2020), RCPred (Legendre et al, 2019), or IRSOM (Platon et al., 2018)), miRNAFold (Tav et al. 2016), miRboost (Tran et al. 2015), and also the ones proposed in the literature.
The project will initially take advantage of molecular data already available (acquired by the partner team or public data): transcriptomics on whole samples and on single cells, genomic alterations, mutations, DNA methylation, histone modifications, DNA accessibility (ATAC-seq), DNA conformation (Hi-C). These data will be associated with clinical and pathological data. During this project, data will be acquired to complement the single cell analyses to take into-account the spatial organization of the tumor (spatial transcriptomics).

The proposed work will make it possible to advance our knowledge of bladder cancer, a cancer for which the therapeutic options in the case of the most aggressive forms remain limited. Since the studied subtypes are found in other cancers, the biological results obtained will have a general scope in oncology. RNAs are promising therapeutic targets, they are also diagnostic markers which can be very specific. In this thesis project the aim will therefore be, in addition to the development of original deep learning algorithms and original bioinformatics methods dedicated to RNAs, to help, thanks to the methods that we will develop, in the analysis and understanding of a health issue, here the bladder cancer, for a better therapeutic response.

Références :
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-Becquey L, Angel E, Tahi F. RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures. Bioinformatics. 2020 Nov 2:btaa944. doi: 10.1093/bioinformatics/btaa944.
-Becquey L, Angel E, Tahi F. BiORSEO: a bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules. Bioinformatics. 2020 Apr 15;36(8):2451-2457. doi: 10.1093/bioinformatics/btz962. PMID: 31913439. .
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-Chantsalnyam, T., Lim, D. Y., Tayara, H., & Chong, K. T. (2020). ncRDeep: Non-coding RNA classification with convolutional neural network. In Computational Biology and Chemistry (Vol. 88). Elsevier Ltd. https://doi.org/10.1016/j.compbiolchem.2020.107364
-Fiannaca, A., La Rosa, M., La Paglia, L., Rizzo, R., Urso, A. (2017). NRC: Non-coding RNA Classifier based on structural features. BioData Mining, 10(1), 27. https://doi.org/10.1186/s13040-017-0148-2
-Geifman, Y. & El-Yaniv, R.. (2019). SelectiveNet: A Deep Neural Network with an Integrated Reject Option. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:2151-2159
-Kamoun A, de Reyniès A, Allory Y, Sjödahl G, Robertson AG, Seiler R, Hoadley KA, Groeneveld CS, Al-Ahmadie H, Choi W, Castro MAA, Fontugne J, Eriksson P, Mo Q, Kardos J, Zlotta A, Hartmann A, Dinney CP, Bellmunt J, Powles T, Malats N, Chan KS, Kim WY, McConkey DJ, Black PC, Dyrskjøt L, Höglund M, Lerner SP, Real FX, Radvanyi F; Bladder Cancer Molecular Taxonomy Group. A Consensus Molecular Classification of Muscle-invasive Bladder Cancer. Eur Urol. 2020 Apr;77(4):420-433. doi: 10.1016/j.eururo.2019.09.006. Epub 2019 Sep 26. PMID: 31563503.
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-Kopp, F., & Mendell, J. T. (2018). Functional Classification and Experimental Dissection of Long Noncoding RNAs. In Cell (Vol. 172, Issue 3, pp. 393–407). https://doi.org/10.1016/j.cell.2018.01.011
-Legendre A, Angel E, Tahi F. RCPred: RNA complex prediction as a constrained maximum weight clique problem. BMC Bioinformatics. 2019 Mar 29;20(Suppl 3):128. doi: 10.1186/s12859-019-2648-1
-Mahé M, Dufour F, Neyret-Kahn H, Moreno-Vega A, Beraud C, Shi M, Hamaidi I, Sanchez-Quiles V, Krucker C, Dorland-Galliot M, Chapeaublanc E, Nicolle R, Lang H, Pouponnot C, Massfelder T, Radvanyi F, Bernard-Pierrot I. An FGFR3/MYC positive feedback loop provides new opportunities for targeted therapies in bladder cancers. EMBO Mol Med. 2018 Apr;10(4):e8163. doi: 10.15252/emmm.201708163. PMID: 29463565.
– Pachera E, Assassi S, Salazar GA, Stellato M, Renoux F, Wunderlin A, Blyszczuk P, Lafyatis R, Kurreeman F, de Vries-Bouwstra J, Messemaker T, Feghali-Bostwick CA, Rogler G, van Haaften WT, Dijkstra G, Oakley F, Calcagni M, Schniering J, Maurer B, Distler JH, Kania G, Frank-Bertoncelj M, Distler O. Long noncoding RNA H19X is a key mediator of TGF-β-driven fibrosis. J Clin Invest. 2020 Sep 1;130(9):4888-4905. doi: 10.1172/JCI135439. PMID: 32603313
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-Platon L, Zehraoui F, Bendahmane A, Tahi F. IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection. Bioinformatics. 2018 Sep 1;34(17):i620-i628. doi: 10.1093/bioinformatics/bty572.
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Profil du candidat :
Candidats avec un niveau de M2 (ou équivalent) en Bioinformatique, informatique ou Sciences des données.

Formation et compétences requises :
Des connaissances en biologie permettront d’interagir plus rapidement avec les biologistes impliqués dans le projet. Certaines de ces connaissances pourront être également acquises au cours des travaux de recherche. Une forte capacité d’adaptation (nouvelles méthodes, nouvelles thématiques) et une envie d’interagir avec des personnes de différentes spécialités sont requises.

Adresse d’emploi :
Bâtiment IBGBI. 23 bv. de France. 91000 Evry.

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : IBISC
Durée : 3 ans
Contact : fariza.tahi@univ-evry.fr
Date limite de publication : 2021-05-17

Contexte :
The objective of the doctoral project will consist, from the initial work carried out in the IBISC and DAVID laboratories, to define, theoretically validate and then experimentally validate an approach for the prediction of the 3D structure of RNA-RNA and RNA-Protein complexes.
Most of the work in structural biology concerns protein molecules. But in recent years, RNAs, which constitute another type of molecules having, like proteins, a 3D conformation, have aroused growing interest. They have various functions, supposedly related to their shape and physicochemical properties, and also to their interactions with other molecules (proteins, as well as RNAs). Awareness of the existence of these non-coding RNAs during the last decade resulted in a renewed interest in studying their structure. For example, they are now being considered as possible therapeutic targets, as are various classes of proteins. Moreover, the determination of the complexes they form when they interact with proteins or with other RNAs would help to better understand their role in diseases like cancer. Many of such complexes, whose structures have been determined by experimental methods like crystallography or NMR, are available in the PDB database (https://www.rcsb.org). However, because of the important cost of these methods, computational methods are needed to make faster the discovery of complexes of RNAs and proteins, by proposing models allowing to predict potential structures that could be validated in a second step by experimental methods.
The computational methods that are proposed in the literature, like GARN2 [9] or RNAcomposer [19], are mostly interested in predicting the 3D structure of an RNA (often based on the energy which must be as low as possible) without taking into account its environment, i.e. its interactions with other RNAs or proteins. However, RNAs are very flexible molecules, and their 3D structure can vary under the effect of these interactions. Therefore, the 3D structure of a given RNA involved in a complex is not always that of minimum energy, since the stability of the complex (i.e. the global energy) is also required. Some methods are therefore proposed for taking into account these interactions, but in our knowledge, all are limited to predicting interaction between two RNAs such as Rascal [23], or between an RNA and a protein such as ICM [1].

Sujet :
In this thesis project we propose to develop methods based on game theory, which would take into account the interdependence between the 3D structure of RNA molecules and the interactions they have with each other and with proteins. We will consider that for each RNA, different possible 3D structures are predicted upstream by existing tools. We will also consider, as a first step, that potential interaction regions are known, eventually predicted. The problem will be modeled as a game in a graph G(V,E), where the vertices (the players) represent the 3D structures of RNAs or proteins, and the edges the possible interactions between the associated 3D structures. Each vertex, or agent, will represent a different RNA, and for each vertex v of V, we know a set of possible 3D structures S_v, and for each 3D structure we know a set of potential interactions areas that may be involved if the RNA represented by v interacts with other RNAs. Each player will have at his disposal 3 sets of actions: he will have to choose a configuration among a (large) set of configurations, a subset of adjacent edges to indicate with which other RNAs he decides to have an interaction, and for each selected edge, its potential interaction area among a set of potential interaction areas calculated beforehand. In order to better guide the search, it will be possible to introduce a distance on the set of 3D structures S_v, similarly we can know in advance that some edges of the graph G will never be used because the corresponding interactions are too weak.
We will look for complexes which are Nash equilibria. To compute Nash equilibria (stable solutions predicting complexes) in such a game theory approach, reinforcement learning and online learning techniques will be used. This approach has been previously used for the computation of 3D RNA structures [8,9]. For this, several algorithms exist,) [2], [3]. The main challenge, compared to classical models, is the definition of the utility functions for the players, that have on one hand to be calculated very quickly, and on the other hand to be sufficiently complex so that the Nash equilibria found are of good quality with respect to a global objective function (energy for example) that is too expensive, in computing time, to optimize. As a first approximation the utility function of a player could be equal to the energy of the configuration he has chosen plus the energies of the interactions with the other RNAs with which he has decided to interact (or a score function related to docking). Another possibility could be to use a multi-criteria approach along with game theory [13]. Indeed, we recently have shown that additional criteria (based on experimental data like SHAPE or based on the satisfaction of user constraints) could be used to improve the predictions of structures (see also BiORSEO [7]), considering insertion of 3D motifs as an additional criterion. The utility functions to be defined must also take into account, for each molecule, any spatial congestion of all of its neighboring molecules, for example by checking intersections of spheres approximating each 3D configuration. Another difficulty is that the actions that players take should have to be symmetrical (an interaction is considered only if both involved molecules choose it). This gives rise to generalized Nash equilibria [10], and the search for decentralized learning algorithms in such a context is a subject of research [24].
The first step of the doctoral project will therefore to finalize the game model described above. Then, it will be a question of correctly identifying and setting up the right online learning approach combined with local optimization methods, in order to make this distributed system converge towards equilibria close to realistic complexes. Finally, the approach finally retained and experimentally validated will be used to treat real cases of RNA-RNA and RNA-protein complexes available in PDB database (https://www.rcsb.org).
Références :
[1] Arnautova Y.A., Abagyan R., Totrov M., Protein-RNA docking using ICM, J. Chem. Theory Comput. 2018
[2] Auer P, Cesa-Bianchi N, Fischer P., Finite-time analysis of the multiarmed bandit problem, Machine learning, 2002; 47(23):235-256.
[3] Auer P, Cesa-Bianchi N, Freund Y, Schapire RE, The nonstochastic multiarmed bandit problem, SIAM Journal on Computing, 2002; 32(1):48–77.
[4] Barth D., Bougueroua S., Gaigeot M.-P., Quessette F., Spezia R., et al. Graph theory for automatic structural recognition in molecular dynamics simulations. The Journal of chemical physics 149 (18), 2018.
[5] de Beauchene IC, de Vries SJ, Zacharias M. Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins, Nucleic Acids Res. 2016;44(10):4565-4580.
[6] Becquey L, Angel E, Tahi F., RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures, Bioinformatics. 2020; btaa944.
[7] Becquey L., Angel E., Tahi F., BiORSEO: A bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules, Bioinformatics, 2020 btz962.
[8] Boudard M, Bernauer J, Barth D, Cohen J, Denise A., GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies, PLoS One. 2015;10(8):e0136444. Published 2015 Aug 27.
[9] Boudard M, Barth D, Bernauer J, Denise A, Cohen J., GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization, Bioinformatics (Oxford, England). 2017 Aug;33(16):2479-2486.
[10] Dutang C., Existence theorems for generalized Nash equilibrium problems: an analysis of assumptions, Journal of Nonlinear Analysis and Optimization, Sompong Dhompongsa and SomyotPlubtieng, 2013, 4 (2), pp.115-126.
[11] Engelen S, Tahi F. Tfold: efficient in silico prediction of non-coding RNA secondary structures, Nucleic Acids Res. 2010;38(7):2453-2466.
[12] Fortunel N.O., Chadli L, Coutier J, Lemaître G, Auvré F, Domingues S, Bouissou-Cadio E, Vaigot P, Cavallero S, Deleuze JF, Roméo PH, and Martin MT, KLF4 inhibition promotes expansion of adult human epidermal precursors and embryonic stem-cell-derived keratinocytes, Nature Biomed Eng, 2019, Dec;3(12): 985-997.
[13] A. Kagrecha et al., Constrained regret minimization for multi-criterion multi-armed bandits, arXiv:2006.09649, juin 2020
[14] Lamiable A., Barth D., Denise A., Quessette F., Vial S., Westhof E.: Automated prediction of three-way junction topological families in RNA secondary structures, Comput. Biol. Chem. 37: 1-5 (2012)
[15] Lamiable A., Quessette F., Vial S., Barth D., Denise A.: An Algorithmic Game-Theory Approach for Coarse-Grain Prediction of RNA 3D Structure, IEEE ACM Trans. Comput. Biol. Bioinform. 10(1): 193-199 (2013)
[16] Legendre A, Angel E, Tahi F., Bi-objective integer programming for RNA secondary structure prediction with pseudoknots, BMC Bioinformatics. Jan 15;19(1) :13. 2018.
[17] Legendre, A., E. Angel, and F. Tahi., RCPred: RNA Complex Prediction as a constrained maximum weight clique problem, BMC Bioinformatics, 2019 Mar 29;20(Suppl 3):128.
[18] Legendre, A., Ibéné M., E. Angel, and F. Tahi, C-RCPred: A multi-objective algorithm for interactive prediction of RNA complexes integrating user knowledge and probing data, to be submitted to ISMB’2021
[19] Popenda, M., Szachniuk, M., Antczak, M., Purzycka, K.J., Lukasiak, P., Bartol, N., Blazewicz, J., Adamiak, R.W., Automated 3D structure composition for large RNAs, Nucleic Acids Research, 2012, 40(14):e112
[20] Tav C, Tempel S, Poligny L, Tahi F. miRNAFold: a web server for fast miRNA precursor prediction in genomes. Nucleic
Acids Res. 2016;44(W1):W181-W184.
[21] Tempel S, Tahi F., A fast ab-initio method for predicting miRNA precursors in genomes, Nucleic Acids Res. 2012;40(11):e80.
[22] A Vulin, M Sedkaoui, S Moratille, N Sevenet, P Soularue, O Rigaud, L Guibbal, J Dulong, P Jeggo, JF Deleuze, J Lamartine and MT Martin. Severe PATCHED1 deficiency in cancer-prone Gorlin patient cells results in intrinsic radiosensitivity. Int J Radiat Oncol Biol Phys.2018,1;102(2):417-425.
[23] Yamasaki S, Hirokawa T, Asai K, Fukui K., Tertiary structure prediction of RNA-RNA complexes using a secondary structure and fragment-based method, J Chem Inf Model. 2014;54:672–682
[24] C. Yu, M. Van der Schaar and A. H. Sayed, Distributed Learning for Stochastic Generalized Nash Equilibrium Problems, IEEE Transactions on Signal Processing, vol. 65, no. 15, pp. 3893-3908, 1 Aug.1, 2017

Profil du candidat :
Candidats avec un niveau Master 2 ou équivalent (3eme année d’ingénieur).

Formation et compétences requises :
Formation de niveau M2 en Informatique (avec une certaine formation en biologie), ou en Bioinformatique / Biologie Computationnelle. Le candidat doit avoir une solide formation en algorithmique et en optimisation combinatoire. Une certaine expérience en bioinformatique structurale serait appréciée.

Adresse d’emploi :
Bâtiment IBGBI. 23 bv. de France. 91000 Evry, France