Perspectives

This Perspective discusses challenges associated with sharing annotated image datasets and offers specific guidance to improve the reuse of bioimages and annotations for AI applications.

This Perspective explores the challenges and future directions in the world of human tumor microbiome research.

This Perspective discusses recent progresses in development of in vitro tumor models and the current challenges in this field.

This Perspective introduces a framework for defining, measuring and reporting resolution in super-resolution microscopy and details the current state of the art in using fluorescence microscopy for structural biology at the ångström scale.

This Perspective explores advances and future directions in spatiotemporal omics methods for four-dimensional profiling.

In this Perspective, Vogg et al. discuss the progress and challenges of using computer vision approaches in the behavioral analysis of primates in their natural environment.

This Perspective outlines the datasets, access methods, data standards, infrastructure, governance and community-engagement strategies of the Human Tumor Atlas Network.

This Perspective provides a roadmap toward globally federated open data resources for bioimaging data.

This Perspective highlights the need to develop methods for single-molecule temporal omics studies and discusses nanopores as a potential solution, as well as the challenges associated with using nanopores for the analysis of complex biological samples.

This Perspective analyzes the most common maturation and assessment techniques for in vitro hiPSC-derived cardiomyocyte models and makes recommendations for standardizations in this field.

This Perspective discusses the integration of small-scale datasets with each other or with larger reference atlases, particularly in the context of single-cell approaches.

This Perspective presents a comprehensive and in-depth overview of computational models based on the deep learning architecture of transformers for single-cell omics analysis.

This primer introduces the elements and best practices of adapting and using language models for biology.

This Perspective discusses the methodologies, application and evaluation of interpretable machine learning (IML) approaches in computational biology, with particular focus on common pitfalls when using IML and how to avoid them.

This Perspective discusses the issue of data leakage in machine learning based models and presents seven questions designed to identify and avoid the problems resulting from data leakage.

This Perspective discusses the methods and tools required for three-dimensional histology in large samples, an approach that promises insights into tissue and organ physiology as well as disease.

This Perspective discusses the potential of protein structure-prediction models for exploring the structural landscape and specificity of TCR–pMHC interactions.

This Perspective provides an overview of major challenges and associated recommendations in computational deconvolution for genomics data.

This Perspective presents a reliable and comprehensive source of information on pitfalls related to validation metrics in image analysis, with an emphasis on biomedical imaging.

Metrics Reloaded is a comprehensive framework for guiding researchers in the problem-aware selection of metrics for common tasks in biomedical image analysis.