Why do authors choose the eLife Model? We spoke to Hironori Funabiki at the Rockefeller University to learn his reasons.

The authors examine the role of Numb, a Notch inhibitor, in intestinal stem cell self-renewal in Drosophila during homeostasis and regeneration. This is an important study providing evidence of a Numb ...

This is a useful study that seeks to address the role of the TET family of DNA demethylation enzymes in pancreatic beta cell senescence in the context of type 2 diabetes (T2DM). Although the concepts ...

Mackie and colleagues present a valuable comparison of lateralized gustation in two well-studied nematodes. The evidence they present that ASEL/R lateralization exists and is achieved by different ...

eLife is a non-profit organisation inspired by research funders and led by scientists. Our mission is to help scientists accelerate discovery by operating a platform for research communication that ...

eLife is a non-profit organisation inspired by research funders and led by scientists. Our mission is to help scientists accelerate discovery by operating a platform for research communication that ...

eLife is a non-profit organisation inspired by research funders and led by scientists. Our mission is to help scientists accelerate discovery by operating a platform for research communication that ...

eLife is a non-profit organisation inspired by research funders and led by scientists. Our mission is to help scientists accelerate discovery by operating a platform for research communication that ...

As Editor-in-Chief, Behrens is responsible for the editorial direction and vision of the journal, providing leadership to ...

Understanding bacterial growth mechanisms can potentially help uncover novel drug targets that are crucial for maintaining cellular viability, particularly for bacterial pathogens. In this important ...

Unveiling the circuit organization and functional roles of endopiriform neurons projecting to the ventral CA1.

This valuable study tests a methodology for the discovery of new honey bee-repellent odorants via machine learning. The conclusions of the study are supported by solid evidence, with predicted ...