386 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- École Polytechnique Fédérale de LausanneCH3 papers
- Radboud University Medical CenterNL3 papers
- Radboud University NijmegenNL3 papers
- Allen Institute for Cell ScienceUS2 papers
- Carl Zeiss (Germany)DE2 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- European Molecular Biology OrganizationDE2 papers
- Harvard UniversityUS2 papers
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do PortoPT2 papers
- Instituto Gulbenkian de CiênciaPT2 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di MilanoIT2 papers
- Max Planck Institute of BiochemistryDE2 papers
13 papers
Ring polymers in two-dimensional melts double-fold around randomly branching "primitive shapes"
Mattia A. Ubertini, Angelo Rosa
Drawing inspiration from the concept of the "primitive path" of a linear chain in melt conditions, we introduce here a numerical protocol which allows us to detect, in an unambiguo…
A Predictive Approach To Enhance Time-Series Forecasting
Skye Gunasekaran, Assel Kembay, Hugo Ladret +4
Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to dat…
Learning and teaching biological data science in the Bioconductor community
Jenny Drnevich, Frederick J. Tan, Fabricio Almeida-Silva +15
Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of…
Community-developed checklists for publishing images and image analysis
Christopher Schmied, Michael Nelson, Sergiy Avilov +51
Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. H…
Fluctuation-driven initialization for spiking neural network training
Julian Rossbroich, Julia Gygax, Friedemann Zenke
Spiking neural networks (SNNs) underlie low-power, fault-tolerant information processing in the brain and could constitute a power-efficient alternative to conventional deep neural…
QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy
Glyn Nelson, Ulrike Boehm, Steve Bagley +97
In April 2020, the QUality Assessment and REProducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scie…
Finding trainable sparse networks through Neural Tangent Transfer
Tianlin Liu, Friedemann Zenke
Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial. The requirements of real biological neural networks are ra…
Effective Model of Loop Extrusion Predicts Chromosomal Domains
M. Crippa, Y. Zhan, G. Tiana
An active loop-extrusion mechanism is regarded as the main out--of--equilibrium mechanism responsible for the structuring of megabase-sized domains in chromosomes. We developed a m…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…
Paradoxical Results of Long-Term Potentiation explained by Voltage-based Plasticity Rule
Claire Meissner-Bernard, Matthias Tsai, Laureline Logiaco +1
Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to under…
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel +1
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these network…
The looping probability of random heteropolymers helps to understand the scaling properties of biopolymers
Y. Zhan, L. Giorgetti, G. Tiana
Random heteropolymers are a minimal description of biopolymers and can provide a theoretical framework to the investigate the formation of loops in biophysical experiments. A two--…
The Feature Importance Ranking Measure
Alexander Zien, Nicole Kraemer, Soeren Sonnenburg +1
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access…