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research.html
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<title>Research interests</title>
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<a class="navbar-brand" href="index.html">Magali Richard</a>
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<h1 class="title toc-ignore">Research interests</h1>
</div>
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<hr />
<div id="tumor-heterogeneity" class="section level2">
<h2>Tumor heterogeneity</h2>
<div class="column-right">
<p><img src="pictures/FIG1.png" width="300px" style="display: block; margin: auto;" /></p>
</div>
<p>A major challenge for current research in oncology is to integrate
data and existing information into a model that takes into account
intra- tumour heterogeneity. Such approach would offer a better
understanding of the biological mechanisms involved in the evolution of
cancer cells, which will improve the development of adapted therapeutic
strategies.</p>
<p>We address this challenge by establishing an original analytical
framework for the study and analysis of complex biological data derived
from tumours, and to provide a novel type of information about
intra-tumour heterogeneity and cancer virulence.</p>
<hr />
</div>
<div id="multimodal-data-integration" class="section level2">
<h2>Multimodal data integration</h2>
<div class="column-right">
<p><img src="pictures/multi-omic_data_integration.png" width="300px" style="display: block; margin: auto;" /></p>
</div>
<p>So far, most statistical methods used for cell deconvolution ignore
the biological relationships between the molecular features used in the
models. Our goal is to provide a statistical framework for deconvolution
including (i) the stochastic dependence across molecular features
induced by the mutual regulation mechanisms; (ii) the a priori knowledge
of the topology of multilayer interaction networks; and (iii) the
similarity between samples that may be induced by controlled
experimental conditions.</p>
<p>We expect that using different types of omic data should improve the
quality of tumor heterogeneity quantification by (i) removing the bias
specific to each type of data and (ii) better identifying the relevant
features in both datasets using joint information provided by both data
types.</p>
<hr />
</div>
<div id="data-challenges-and-benchmarks" class="section level2">
<h2>Data challenges and Benchmarks</h2>
<div class="column-right">
<p><img src="pictures/data_challenge_benchmarks.png" width="300px" style="display: block; margin: auto;" /></p>
</div>
<p>Codabench is an open-source, web-based data challenge platform
primarily utilized by the machine learning community to orchestrate
public competitions in the field data science analysis. Codabench offers
the possibily to organize flexible competitions and benchmark, thus
contributing to the development of advanced methods in data analysis and
promoting reproducibility of results. In addition, it facilitates
hands-on learning and fosters collaboration within the scientific
community.</p>
<p>We contribute to the development of codabench and organize regular
data challenge in Health. Our next objective would be to establish a
federation of Codabench platforms across various academic sites,
facilitating the sustainable growth of the platform while ensuring the
capability to archive and retrieve each past competition.</p>
<hr />
</div>
<div id="epigenetic-regulation-and-variability" class="section level2">
<h2>(Epi)genetic regulation and variability</h2>
<div class="column-right">
<p><img src="pictures/penda.png" width="300px" style="display: block; margin: auto;" /></p>
</div>
<p>Combining statistical analysis and quantitative mathematical modeling
with molecular biology experiments on specific cell lines and on tumors,
we aim at discovering epigenetically regulated genomic domains in lung
cancer, as well as at characterizing and modeling these epigenetic “hot”
domains and their association with tumor progression and
aggressiveness.</p>
</div>
</div>
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