[HTML][HTML] How to build the virtual cell with artificial intelligence: Priorities and opportunities
Cells are essential to understanding health and disease, yet traditional models fall short of
modeling and simulating their function and behavior. Advances in AI and omics offer …
modeling and simulating their function and behavior. Advances in AI and omics offer …
Instrumentation at the leading edge of proteomics
The proteome, or collection of proteoforms expressed in a biological system, is dynamic and
heterogeneous. As our appreciation for the complexity of the proteome has evolved, so have …
heterogeneous. As our appreciation for the complexity of the proteome has evolved, so have …
Macromolecular condensation organizes nucleolar sub-phases to set up a pH gradient
Nucleoli are multicomponent condensates defined by coexisting sub-phases. We identified
distinct intrinsically disordered regions (IDRs), including acidic (D/E) tracts and K-blocks …
distinct intrinsically disordered regions (IDRs), including acidic (D/E) tracts and K-blocks …
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
Most neural networks assume that input images have a fixed number of channels (three for
RGB images). However, there are many settings where the number of channels may vary …
RGB images). However, there are many settings where the number of channels may vary …
Cell maps for artificial intelligence: AI-ready maps of human cell architecture from disease-relevant cell lines
This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals,
methods, standards, current datasets, software tools, status, and future directions. CM4AI is …
methods, standards, current datasets, software tools, status, and future directions. CM4AI is …
[HTML][HTML] Unbiased single-cell morphology with self-supervised vision transformers
Accurately quantifying cellular morphology at scale could substantially empower existing
single-cell approaches. However, measuring cell morphology remains an active field of …
single-cell approaches. However, measuring cell morphology remains an active field of …
From pixels to insights: Machine learning and deep learning for bioimage analysis
M Jan, A Spangaro, M Lenartowicz… - BioEssays, 2024 - Wiley Online Library
Bioimage analysis plays a critical role in extracting information from biological images,
enabling deeper insights into cellular structures and processes. The integration of machine …
enabling deeper insights into cellular structures and processes. The integration of machine …
Self-supervision advances morphological profiling by unlocking powerful image representations
Morphological profiling is a powerful technology that enables unbiased characterization of
cellular states through image-based screening. Inspired by recent progress in self …
cellular states through image-based screening. Inspired by recent progress in self …
Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms
The development of a reference atlas of the healthy human body requires automated image
segmentation of major anatomical structures across multiple organs based on spatial …
segmentation of major anatomical structures across multiple organs based on spatial …
Machine learning in healthcare citizen science: A sco** review
Objectives This sco** review aims to clarify the definition and trajectory of citizen-led
scientific research (so-called citizen science) within the healthcare domain, examine the …
scientific research (so-called citizen science) within the healthcare domain, examine the …