Shantanu Singh

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.

The paper optimizes biomaterial surface topographies using evolutionary algorithms inspired by natural selection to enhance osteogenic marker expression.

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.

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.

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.

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.