Michael Bon
Centre national de la recherche scientifique
7 Papers
69 Citations
Michael Bon is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Pseudoknot & Poisson–Boltzmann equation. The author has an hindex of 5, co-authored 6 publications. Previous affiliations of Michael Bon include French Alternative Energies and Atomic Energy Commission.
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Papers
Topological classification of RNA structures.
TL;DR: In this paper, a novel topological classification of RNA secondary structures with pseudoknots is presented, based on the topological genus of the circular diagram associated to the RNA base-pair structure.
139
Incorporating dipolar solvents with variable density in Poisson-Boltzmann electrostatics.
TL;DR: In this paper, a new way to calculate the electrostatic properties of macromolecules was proposed, which goes beyond the classical Poisson-Boltzmann treatment with only a small extra CPU cost.
95
•Journal Article
Incorporating dipolar solvents with variable density in Poisson-Boltzmann electrostatics. Biophysj. J. 95:5587-5605.
TL;DR: A new way to calculate the electrostatic properties of macromolecules that goes beyond the classical Poisson-Boltzmann treatment with only a small extra CPU cost is described and qualitative agreement on a coarse-grained level is shown.
69
An in-depth benchmark study of the CATE estimation problem: experimental framework, metrics and models Version 1
TL;DR: A rich benchmark study whose general ambition is to to achieve good predictions of the CATE with machine learning techniques and designs a special structure for the benchmark and introduces axes of analysis to explore the global and local behaviours of several models.
•Posted Content
TT2NE: A novel algorithm to predict RNA secondary structures with pseudoknots
Michael Bon,Henri Orland +1 more
TL;DR: Analysis of TT2NE's incorrect predictions sheds light on the need to study how sterical constraints limit the range of pseudoknotted structures that can be formed from a given sequence, and compares with state-of-the-art algorithms.