Perspectives

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.

This Perspective introduces advances in quantitative phase imaging and artificial intelligence-based image analysis and further describes how the two technologies intersect and synergize to enable biomedical research.

Community-developed checklists offer best-practice guidance for biologists preparing light microscopy images and describing image analyses for publications.

This Perspective introduces the Minimum Information About Disorder Experiments guidelines, which provide a community consensus on the minimum information required to appropriately describe metadata on experimentally and computationally derived structural state(s) of intrinsically disordered proteins or regions.

This Perspective discusses how machine learning can help in studying rare diseases using various emerging approaches.

This Perspective introduces biologists interested in computational approaches to the benefits of the Julia programming language for meeting current and future computational demands.

A community of researchers working in the emerging field of single-cell proteomics propose best-practice experimental and computational recommendations and reporting guidelines for studies analyzing proteins from single cells by mass spectrometry.

This Perspective discusses available software tools for lipidomics data analysis and provides a web-based Lipidomics Tools Guide to help guide the choice of these tools, organized by the major tasks for lipidomics research.

This Perspective describes common pitfalls that can occur when using light sheet microscopy and offers guidance for improved quantitative imaging with these instruments.

In this Perspective, technologies and challenges in the cardiac tissue engineering field are discussed and strategies to overcome these challenges are proposed.

This Perspective focuses on experimental techniques developed for circRNA studies, and provides practical guidelines for performing circRNA research.