Reconstructing cell cycle and disease progression using deep learning

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

(2017) Holger Hennig et al.   METHODS

CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets

(2016) David Dao et al.   BIOINFORMATICS

Classifying and segmenting microscopy images with deep multiple instance learning

(2016) Oren Z. Kraus et al.   BIOINFORMATICS

Diffusion pseudotime robustly reconstructs lineage branching

(2016) Laleh Haghverdi et al.   NATURE METHODS

Label-free cell cycle analysis for high-throughput imaging flow cytometry

(2016) Thomas Blasi et al.   Nature Communications

Deep Learning in Label-free Cell Classification

(2016) Claire Lifan Chen et al.   Scientific Reports

Deep learning

(2015) Yann LeCun et al.   NATURE

Trajectories of cell-cycle progression from fixed cell populations

(2015) Gabriele Gut et al.   NATURE METHODS

Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

(2014) Sean C. Bendall et al.   CELL

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

(2014) Cole Trapnell et al.   NATURE BIOTECHNOLOGY

Multiplexed ion beam imaging of human breast tumors

(2014) Michael Angelo et al.   NATURE MEDICINE

Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators

(2012) Bernd Bodenmiller et al.   NATURE BIOTECHNOLOGY

Biological imaging software tools

(2012) Kevin W Eliceiri et al.   NATURE METHODS

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning

(2009) Thouis R. Jones et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA