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Network analysis of genomic and clinical data

Abstract : This thesis consists in the development of a novel methodological approach to reconstruct networks starting from biological and clinical data. It overcomes some technical and computational problems of existing methods to accomplish this task. Our algorithm (MIIC), allows the study of discrete, continuous and mixed datasets with any type of probability and density distributions, including the possible presence of latent variables, which are very important in real contexts where it is not always possible to collect all relevant variables. MIIC is available through a web interface at the address:, and as an R package available on CRAN. The second part of the thesis is devoted to the analysis of real life applications: from gene regulatory network reconstruction and protein contact map reconstruction, to the study of clinical records of patients affected by cognitive disorders or breast cancer. MIIC can help physicians in visualizing and analysing direct, indirect and possibly causal effects from patient medical records, discovering novel unexpected direct interdependencies between clinically relevant information or explaining a missing connection through other links found in the reconstruction.
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Submitted on : Monday, February 15, 2021 - 10:56:12 AM
Last modification on : Wednesday, February 17, 2021 - 3:30:58 AM


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  • HAL Id : tel-03141274, version 1


Nadir Sella. Network analysis of genomic and clinical data. Bioinformatics [q-bio.QM]. Sorbonne Université, 2019. English. ⟨NNT : 2019SORUS351⟩. ⟨tel-03141274⟩



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