Anne Elizabeth Carpenter

The paper develops an automated high-content synaptic phenotyping platform in human neurons and astrocytes, identifying BET inhibitors as key regulators of synapse assembly.

The paper predicts compound activity using chemical structures, images, and gene-expression profiles, showing that combining these modalities can improve prediction accuracy in drug discovery.

Cell Painting images and profiles for 115,796 compounds in U2OS cells from 12 laboratories; 15.2% of analyzed compounds are phenotypically active, and cross-laboratory replicates cut reference-compound activity by 34%.

The paper provides four datasets combining gene expression and morphology profiles from over 28,000 genetic and chemical perturbations for multi-modal analysis.

The paper compares morphology and gene expression data to determine their complementary roles in mapping cell states across drug perturbations.

The paper identifies small molecule regulators of biological pathways by matching compound effects to known pathway phenotypes in public cell image data.

The paper introduces a cloud-based platform for analyzing calcium activity in motor neurons to identify compounds that reverse disease phenotypes in amyotrophic lateral sclerosis.

The paper summarizes a survey of 484 bioimage analysis community members to identify needs for tools, training, and resources.

The paper develops a method to predict cell health phenotypes from Cell Painting images using machine learning, validated across multiple perturbations and assays.

The paper uses multiplexed fluorescence imaging and machine learning to identify synaptic subtypes and examine their changes under activity suppression.

This study investigates how cell shape affects the phenotypic response of human mesenchymal stem cells and identifies molecular mechanisms linking shape to function.

This study uses deep learning and label-free imaging flow cytometry to classify red blood cell morphologies during storage, achieving 90% accuracy and predicting expiration dates based on morphological changes.

The paper introduces a deep learning framework for nucleus segmentation that uses image style transfer to adapt to new data without expert annotations, outperforming existing methods on diverse microscopy images.

This study evaluates deep learning strategies for nucleus segmentation in fluorescence images, showing U-Net outperforms classical methods and reduces biologically relevant errors.

The paper improves image-based profiling by fusing data to capture cell heterogeneity, enhancing prediction of compound mechanisms of action and gene pathways by ~20%.

This paper develops a weakly supervised CNN approach for learning single-cell feature embeddings from microscopy images, achieving better accuracy than classical features and competitiveness with transfer learning methods.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

Adversarially learned features group fluorescence images of chemically treated human cells better than autoencoder features, though classical computer vision and transfer learning still do better.

This paper uses deep learning to reconstruct cell cycle stages and disease progression from image data, achieving lower error rates and faster analysis.

The paper uses morphological profiling to identify gene function and connectivity, revealing TRAF2/C-REL's negative regulation of Hippo pathway effectors.

ImageNet-pretrained Inception-v3, without fine-tuning or segmentation, reaches 91% mechanism-of-action accuracy on illumination-corrected BBBC021 versus 90% for classical mean profiles, running in 22 minutes versus over 10 hours for single-core CellProfiler.