High-throughput phenotyping is becoming increasingly available thanks to analytical and
bioinformatics approaches that enable the use of very high-dimensional data and to the …

Combining microfluidics technology with machine learning represents an innovative
approach to conduct massive quantitative cell behavior study and implement smart decision …

We describe a novel method to achieve a universal, massive, and fully automated analysis
of cell motility behaviours, starting from time-lapse microscopy images. The approach was …

One of the major problems in bioimaging, often highly underestimated, is whether features
extracted for a discrimination or regression task will remain valid for a broader set of similar …

Biological experiments based on organ-on-chips (OOCs) exploit light Time-Lapse
Microscopy (TLM) for a direct observation of cell movement that is an observable signature …

The increasing interest for microfluidic devices in medicine and biology has opened the way
to new time-lapse microscopy era where the amount of images and their acquisition time will …

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a
signature of underlying biological phenomena. The heterogeneity in cell response, due to …

The derivation of input–output relationships in deep learning architectures is mostly a black-
box process, in which uninformative or confounding factors might bias the classification …

The emerging tumor-on-chip (ToC) approaches allow to address biomedical questions out
of reach with classical cell culture techniques: in biomimetic 3D hydrogels they partially …

Microfluidics represents a very promising technological solution for conducting massive
biological experiments. However, the difficulty of managing the amount of information …