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Considering the increasing number of papers in this field, we roughly summarize some articles and put them into the following categories:
- [2024] Morphological profiling for drug discovery in the era of deep learning (Briefings in Bioinformatics) [paper]
- [2024] Artificial intelligence for high content imaging in drug discovery (Current Opinion in Structural Biology) [paper]
- [2023] Deep learning in image-based phenotypic drug discovery (Trends in Cell Biology) [paper]
- [2022] Phenotypic drug discovery: recent successes, lessons learned and new directions (Nature Reviews Drug Discovery) [paper]
- [2020] Image-based profiling for drug discovery: due for a machine-learning upgrade? (Nature Reviews Drug Discovery) [paper]
- [Cell Painting Gallery] [Link] [Datasets Overview] [AWS Overview]
- [JUMP-Cell Painting Consortium] [Link]
- [NYSCF Automated Deep Phenotyping Dataset (ADPD)] [Link]
- [DeepProfiler] Learning representations for image-based profiling of perturbations (Nature Communications) [paper] [code]
- [Pycytominer] Reproducible image-based profiling with Pycytominer (Arxiv) [paper] [code]
- [CellProfiler] CellProfiler 3.0:Next-generation imageprocessing for biology (PLOS BIOLOGY) [paper] [code]
Details
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[Batch Correction] Evaluating batch correction methods for image-based cell profiling (Nature Communications) [paper] [code]
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[scDINO] Self-supervised vision transformers accurately decode cellular state heterogeneity (BioRxiv) [paper][code]
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[MIGA] Cross-Modal Graph Contrastive Learning with Cellular Images (Advanced science) [paper] [code]
- Deep representation learning determines drug mechanism of action from cell painting images (Digital Discovery) [paper] [code]
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[Dataset] Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts (Nature Communications) [paper] [dataset]
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[WS-DINO] Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels (LMRL @NeurIPS 2022) [paper] [code]
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[Contrastive learning] Contrastive learning of image- and structure-based representations in drug discovery (MLDD @ICLR 2022) [paper] [code]
Details
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[PertKGE] Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics (Cell Genomics) [paper] [code]
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[PERCEPTION] PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors (Nature Cancer) [paper] [code]
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[SCAD] Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD (Advanced Science) [paper] [code]
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[ASGARD] ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs (Nature Communications) [paper] [code]
Details
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[Datasets] JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations (Arxiv) [paper] [dataset]
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[Datasets and Benchmark] Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations (Nature Methods) [paper] [code]
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[Multimodal] Learning Molecular Representation in a Cell (Arxiv) [paper] [code]
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[Multimodal] Removing Biases from Molecular Representations via Information Maximization (Arxiv) [paper] [code]
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[Multimodal] Multimodal data fusion for cancer biomarker discovery with deep learning (Nature Machine Intelligence) [paper]
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[Multimodal] Predicting compound activity from phenotypic profiles and chemical structures (Nature Communications) [paper] [code]
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[Multimodal deep learning] Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning (Cancer Cell) [paper] [code]
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[Datasets and Benchmark] High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations (Nature Methods) [paper] [code]
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Morphology and gene expression profiling provide complementary information for mapping cell state (Cell Systems) [paper] [code]
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Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection (Communications Biology) [paper] [code]
Details
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[ChannelViT] Channel Vision Transformer: An Image Is Worth C x 16 x 16 Words (ICLR 2024) [paper] [code]
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[CHAMMI] CHAMMI: A benchmark for channel-adaptive models in microscopy imaging (NIPS 2024) [paper] [code]
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[DiChaViT] Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers (ArxiV) [paper]
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[ChAda-ViT] ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images (CVPR 2024) [paper] [code]
