Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

1 code implementation • Nature Communications 2020 • Jan Zrimec, Christoph S. Börlin, Filip Buric, Azam Sheikh Muhammad, Rhongzen Chen, Verena Siewers, Vilhelm Verendel, Jens Nielsen, Mats Töpel, Aleksej Zelezniak

Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

A Review of Gas-Surface Interaction Models for Orbital Aerodynamics Applications

no code implementations • 1 Oct 2020 • Sabrina Livadiotti, Nicholas H. Crisp, Peter C. E. Roberts, Stephen D. Worrall, Vitor T. A. Oiko, Steve Edmondson, Sarah J. Haigh, Claire Huyton, Katharine L. Smith, Luciana A. Sinpetru, Brandon E. A. Holmes, Jonathan Becedas, Rosa María Domínguez, Valentín Cañas, Simon Christensen, Anders Mølgaard, Jens Nielsen, Morten Bisgaard, Yung-An Chan, Georg H. Herdrich, Francesco Romano, Stefanos Fasoulas, Constantin Traub, Daniel Garcia-Almiñana, Silvia Rodriguez-Donaire, Miquel Sureda, Dhiren Kataria, Badia Belkouchi, Alexis Conte, Jose Santiago Perez, Rachel Villain, Ron Outlaw

Limits and assets of each model have been discussed with regards to the context of this paper.

Space Physics

Performance of regression models as a function of experiment noise

2 code implementations • 17 Dec 2019 • Gang Li, Jan Zrimec, Boyang Ji, Jun Geng, Johan Larsbrink, Aleksej Zelezniak, Jens Nielsen, Martin KM Engqvist

A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on a particular dataset, or whether further model improvement is possible.

regression