eLife

Latest preprint reviews

1. Five years later, with double the demographic data, naked mole-rat mortality rates continue to defy Gompertzian laws by not increasing with age

   This article has 3 authors:

   1. J. Graham Ruby
   2. Megan Smith
   3. Rochelle Buffenstein

   This article has been curated by 1 group:

   • Curated by eLife

     eLife assessment

     This useful study aims to characterize patterns of demographic aging in naked mole rats by quantifying mortality rates in a captive colony, up to approximately the median age of death. The study system is a fascinating case of unusual longevity and physiology in mammals, but because of limited sampling at older ages and missing analyses, the evidence for the main conclusion – that naked mole rats do not experience actuarial senescence – is incomplete for younger animals and inadequate for older animals. The work nevertheless provides data of interest to biodemographers and biomedical researchers interested in naked mole rats as a model for aging.

   Reviewed by eLife

2. Broad functional profiling of fission yeast proteins using phenomics and machine learning

   This article has 9 authors:

   1. María Rodríguez-López
   2. Nicola Bordin
   3. Jon Lees
   4. Harry Scholes
   5. Shaimaa Hassan
   6. Quentin Saintain
   7. Stephan Kamrad
   8. Christine Orengo
   9. Jürg Bähler

   This article has been curated by 1 group:

   • Curated by eLife

     eLife assessment

     This important study combines extensive phenotyping of genome-wide deletion mutants and machine learning-based prediction to generate a large scale resource for understanding the functions of thousands of fission yeast protein-coding genes. This resource is supported by convincing phenotyping data and state-of-the-art bioinformatic analyses and will be of interest to many geneticists.

   Reviewed by eLife

3. We couldn't get details of this article at this time:
   10.1101/2023.04.12.536539

4. We couldn't get details of this article at this time:
   10.1101/2022.12.22.521681

5. Spatial transformation of multi-omics data unlocks novel insights into cancer biology

   This article has 6 authors:

   1. Mateo Sokač
   2. Asbjørn Kjær
   3. Lars Dyrskjøt
   4. Benjamin Haibe-Kains
   5. Hugo JWL Aerts
   6. Nicolai J Birkbak

   This article has been curated by 1 group:

   • Curated by eLife

     eLife assessment

     This valuable manuscript presents a new approach to transform multi-omics datasets into images and to exploit Deep Learning methods for image analysis of the transformed datasets. As an example, the method is applied to multi-omics datasets on different cancers. While the evidence in this specific case is solid, whether the method is working as advertised in other settings is not yet known.

   Reviewed by eLife

6. We couldn't get details of this article at this time:
   10.1101/2023.04.10.536204

7. We couldn't get details of this article at this time:
   10.1101/2023.03.20.533514

8. We couldn't get details of this article at this time:
   10.1101/2022.12.15.520590

9. We couldn't get details of this article at this time:
   10.1101/2023.03.17.533096

10. We couldn't get details of this article at this time:
    10.1101/2023.03.16.532982