References of "De Landtsheer, Sébastien"
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See detailDegree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.
Badkas, Apurva UL; Nguyen, Thanh-Phuong UL; Caberlotto, Laura et al

in Biology (2021), 10(2),

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic ... [more ▼]

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities. [less ▲]

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See detailTopological network measures for drug repositioning.
Badkas, Apurva UL; De Landtsheer, Sébastien; Sauter, Thomas UL

in Briefings in bioinformatics (2020)

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these ... [more ▼]

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning. [less ▲]

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See detailUsing Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.
De Landtsheer, Sébastien; Lucarelli, Philippe UL; Sauter, Thomas UL

in Frontiers in physiology (2018), 9

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down ... [more ▼]

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information. [less ▲]

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