{"total":47,"rows":[{"id":"2409.11654","title":"How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities","created":"2024-09-18","cited_by_count":57,"area":null},{"id":"2404.11568","title":"On the Scalability of GNNs for Molecular Graphs","created":"2024-04-17","cited_by_count":36,"area":null},{"id":"2408.06687","title":"Masked Image Modeling: A Survey","created":"2024-08-13","cited_by_count":29,"area":null},{"id":"2410.13956","title":"Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all","created":"2024-10-17","cited_by_count":19,"area":null},{"id":"2411.02572","title":"ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy","created":"2024-11-04","cited_by_count":13,"area":null},{"id":"2503.20158","title":"RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy","created":"2025-03-26","cited_by_count":11,"area":null},{"id":"2505.14919","title":"TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction","created":"2025-05-20","cited_by_count":9,"area":null},{"id":"2502.02307","title":"UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training","created":"2025-02-04","cited_by_count":8,"area":null},{"id":"2506.06290","title":"CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning","created":"2025-05-16","cited_by_count":7,"area":null},{"id":"2601.16011","title":"THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications","created":"2026-01-22","cited_by_count":7,"area":null},{"id":"2409.08302","title":"How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval","created":"2024-09-10","cited_by_count":6,"area":null},{"id":"2501.06039","title":"AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery","created":"2025-01-10","cited_by_count":6,"area":null},{"id":"2503.19331","title":"ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning","created":"2025-03-25","cited_by_count":6,"area":null},{"id":"2603.11970","title":"Statistical and structural identifiability in representation learning","created":"2026-03-12","cited_by_count":6,"area":null},{"id":"2412.16247","title":"Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models","created":"2024-12-20","cited_by_count":4,"area":null},{"id":"2504.13442","title":"SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion","created":"2025-04-18","cited_by_count":4,"area":null},{"id":"2510.01298","title":"MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging","created":"2025-10-01","cited_by_count":3,"area":null},{"id":"2504.09544","title":"Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling","created":"2025-04-13","cited_by_count":2,"area":null},{"id":"2505.18745","title":"C3R: Channel Conditioned Cell Representations for unified evaluation in microscopy imaging","created":"2025-05-24","cited_by_count":2,"area":null},{"id":"2509.19896","title":"Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2603.00143","title":"GrapHist: Graph Self-Supervised Learning for Histopathology","created":"2026-02-24","cited_by_count":2,"area":null},{"id":"2502.19568","title":"PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery","created":"2025-02-26","cited_by_count":1,"area":null},{"id":"2507.01347","title":"Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation","created":"2025-07-02","cited_by_count":1,"area":null},{"id":"2512.20833","title":"CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images","created":"2025-12-23","cited_by_count":1,"area":null},{"id":"2602.05527","title":"Generalization of Self-Supervised Vision Transformers for Protein Localization Across Microscopy Domains","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2605.20534","title":"Axiomatizing Neural Networks via Pursuit of Subspaces","created":"2026-05-19","cited_by_count":1,"area":null},{"id":"2608.07632","title":"JUMP-lite: Compact, reproducible benchmarking of cell representations","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2503.19149","title":"Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopy","created":"2025-03-24","cited_by_count":0,"area":null},{"id":"2509.06986","title":"CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2510.21337","title":"Morphologically Intelligent Perturbation Prediction with FORM","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2512.22423","title":"Bright 4B: Scaling Hyperspherical Learning for Segmentation in 3D Brightfield Microscopy","created":"2025-12-27","cited_by_count":0,"area":null},{"id":"2601.22331","title":"Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.04585","title":"ImmuVis: Hyperconvolutional Foundation Model for Imaging Mass Cytometry","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2603.05622","title":"Adversarial Batch Representation Augmentation for Batch Correction in High-Content Cellular Screening","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.13377","title":"Deep Learning for BioImaging: What Are We Really Learning?","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2603.13401","title":"MAD: Microenvironment-Aware Distillation -- A Pretraining Strategy for Virtual Spatial Omics from Microscopy","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2604.20824","title":"Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2604.22832","title":"Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.15383","title":"MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.31562","title":"Effective Biological Representation Learning by Masking Gene Expression","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.09601","title":"Assessing Sample Quality in Conditional Generation under Compositional Shift","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.16477","title":"AURA: Active-Response Attribution under Treatment Ambiguity in Bacterial Cytological Profiling","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.23964","title":"3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2606.29883","title":"Building artificial intelligence virtual tissue (AIVT) for tissue state representation, feature prediction, and dynamic simulation","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2607.04353","title":"HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy","created":"2026-07-05","cited_by_count":0,"area":null},{"id":"2608.16810","title":"Unsupervised Learning of Cell Instances with Generative Routing Pyramids","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.12761","title":"Same Encoder, Different Winner: A Paired-View Framework for Cell Painting Encoder Evaluation","created":"2026-09-11","cited_by_count":0,"area":null}],"next_cursor":null}