Continuous analysis of single cells, over several cell divisions and for up to weeks at a time,
is crucial to deciphering rare, dynamic and heterogeneous cell responses, which would …

Background The life sciences, biomedicine and health care are increasingly turning into a
data intensive science [2–4]. Particularly in bioinformatics and computational biology we …

This work presents a generic computer vision system designed for exploiting trained deep
Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these …

Motivation Identifying phenotypes based on high-content cellular images is challenging.
Conventional image analysis pipelines for phenotype identification comprise multiple …

This work presents a system based on an ensemble of Convolutional Neural Networks
(CNNs) and descriptors for bioimage classification that has been validated on different …

Abstract Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial
Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine …

Background Protein function in eukaryotic cells is often controlled in a cell cycle-dependent
manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a …

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of
large datasets for robust training sessions and no overfitting makes them hard to apply in …

A problem with convolutional neural networks (CNNs) is that they require large datasets to
obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods …

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative
platform for drug discovery. This review provides an overview of the various model systems …