Emerging AI Approaches for Cancer Spatial Omics | Paper Citation Record

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This paper cites The human body at cellular resolution: the NIH Human Biomolecular Atlas Program,.

Emerging AI Approaches for Cancer Spatial Omics The human body at cellular resolution: the NIH Human Biomolecular Atlas Program,

Reference 1

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This paper cites Tumour atlases enable researchers to navigate through cancers,.

Emerging AI Approaches for Cancer Spatial Omics Tumour atlases enable researchers to navigate through cancers,

Reference 2

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This paper cites Spatial mapping of cellular senescence: emerging challenges and opportunities,.

Emerging AI Approaches for Cancer Spatial Omics Spatial mapping of cellular senescence: emerging challenges and opportunities,

Reference 3

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This paper cites CROST: a comprehensive repository of spatial transcriptomics,.

Emerging AI Approaches for Cancer Spatial Omics CROST: a comprehensive repository of spatial transcriptomics,

Reference 4

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This paper cites STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization,.

Emerging AI Approaches for Cancer Spatial Omics STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization,

Reference 5

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This paper cites RudolfV: A Foundation Model by Pathologists for Pathologists.

Emerging AI Approaches for Cancer Spatial Omics RudolfV: A Foundation Model by Pathologists for Pathologists

Reference 6

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Emerging AI Approaches for Cancer Spatial Omics An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Emerging AI Approaches for Cancer Spatial Omics DINOv2: Learning Robust Visual Features without Supervision

Reference 8

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This paper cites Virchow: A Million-Slide Digital Pathology Foundation Model.

Emerging AI Approaches for Cancer Spatial Omics Virchow: A Million-Slide Digital Pathology Foundation Model

Reference 9

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This paper cites Towards a general-purpose foundation model for computational pathology,.

Emerging AI Approaches for Cancer Spatial Omics Towards a general-purpose foundation model for computational pathology,

Reference 10

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This paper cites Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation.

Emerging AI Approaches for Cancer Spatial Omics Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 11

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This paper cites From whole-slide image to biomarker prediction: end-to- end weakly supervised deep learning in computational pathology,.

Emerging AI Approaches for Cancer Spatial Omics From whole-slide image to biomarker prediction: end-to- end weakly supervised deep learning in computational pathology,

Reference 12

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This paper cites Language Models are Few-Shot Learners.

Emerging AI Approaches for Cancer Spatial Omics Language Models are Few-Shot Learners

Reference 13

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Emerging AI Approaches for Cancer Spatial Omics BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 14

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Emerging AI Approaches for Cancer Spatial Omics LLaMA: Open and Efficient Foundation Language Models

Reference 15

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This paper cites A visual-language foundation model for computational pathology,.

Emerging AI Approaches for Cancer Spatial Omics A visual-language foundation model for computational pathology,

Reference 16

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This paper cites A multimodal generative AI copilot for human pathology,.

Emerging AI Approaches for Cancer Spatial Omics A multimodal generative AI copilot for human pathology,

Reference 17

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This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Emerging AI Approaches for Cancer Spatial Omics Multimodal Whole Slide Foundation Model for Pathology

Reference 18

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This paper cites Deep learning in spatially resolved transcriptomics: a comprehensive technical view,.

Emerging AI Approaches for Cancer Spatial Omics Deep learning in spatially resolved transcriptomics: a comprehensive technical view,

Reference 19

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This paper cites Transformers in single-cell omics: a review and new perspectives,.

Emerging AI Approaches for Cancer Spatial Omics Transformers in single-cell omics: a review and new perspectives,

Reference 20

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This paper cites Language of Stains: Tokenization Enhances Multiplex Immunofluorescence and Histology Image Synthesis | bioRxiv.

Emerging AI Approaches for Cancer Spatial Omics Language of Stains: Tokenization Enhances Multiplex Immunofluorescence and Histology Image Synthesis | bioRxiv

Reference 21

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This paper cites scGPT: toward building a foundation model for single-cell multi-omics using generative AI,.

Emerging AI Approaches for Cancer Spatial Omics scGPT: toward building a foundation model for single-cell multi-omics using generative AI,

Reference 22

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This paper cites scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data,.

Emerging AI Approaches for Cancer Spatial Omics scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data,

Reference 23

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This paper cites Gene2vec: distributed representation of genes based on co-expression,.

Emerging AI Approaches for Cancer Spatial Omics Gene2vec: distributed representation of genes based on co-expression,

Reference 24

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This paper cites Large-scale foundation model on single-cell transcriptomics,.

Emerging AI Approaches for Cancer Spatial Omics Large-scale foundation model on single-cell transcriptomics,

Reference 25

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This paper cites CellPLM: Pre-training of Cell Language Model Beyond Single Cells,.

Emerging AI Approaches for Cancer Spatial Omics CellPLM: Pre-training of Cell Language Model Beyond Single Cells,

Reference 26

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This paper cites Generative pretraining from large-scale transcriptomes for single-cell deciphering,.

Emerging AI Approaches for Cancer Spatial Omics Generative pretraining from large-scale transcriptomes for single-cell deciphering,

Reference 27

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This paper cites xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data.

Emerging AI Approaches for Cancer Spatial Omics xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

Reference 28

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This paper cites Transformer for one stop interpretable cell type annotation,.

Emerging AI Approaches for Cancer Spatial Omics Transformer for one stop interpretable cell type annotation,

Reference 29

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This paper cites Transfer learning enables predictions in network biology,.

Emerging AI Approaches for Cancer Spatial Omics Transfer learning enables predictions in network biology,

Reference 30

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This paper cites Nicheformer: a foundation model for single-cell and spatial omics,.

Emerging AI Approaches for Cancer Spatial Omics Nicheformer: a foundation model for single-cell and spatial omics,

Reference 31

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This paper cites Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation.

Emerging AI Approaches for Cancer Spatial Omics Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

Reference 32

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This paper cites stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement.

Emerging AI Approaches for Cancer Spatial Omics stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement

Reference 33

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This paper cites Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer,.

Emerging AI Approaches for Cancer Spatial Omics Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer,

Reference 34

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This paper cites Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer,.

Emerging AI Approaches for Cancer Spatial Omics Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer,

Reference 35

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This paper cites Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging,.

Emerging AI Approaches for Cancer Spatial Omics Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging,

Reference 36

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This paper cites Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction,.

Emerging AI Approaches for Cancer Spatial Omics Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction,

Reference 37

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This paper cites Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry,.

Emerging AI Approaches for Cancer Spatial Omics Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry,

Reference 38

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This paper cites IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues,.

Emerging AI Approaches for Cancer Spatial Omics IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues,

Reference 39

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This paper cites Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments,.

Emerging AI Approaches for Cancer Spatial Omics Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments,

Reference 40

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This paper cites Prediction of Outcome from Spatial Protein Profiling of Triple- Negative Breast Cancers,.

Emerging AI Approaches for Cancer Spatial Omics Prediction of Outcome from Spatial Protein Profiling of Triple- Negative Breast Cancers,

Reference 41

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This paper cites High-dimensional imaging using combinatorial channel multiplexing and deep learning,.

Emerging AI Approaches for Cancer Spatial Omics High-dimensional imaging using combinatorial channel multiplexing and deep learning,

Reference 42

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This paper cites Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes,.

Emerging AI Approaches for Cancer Spatial Omics Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes,

Reference 43

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This paper cites A Foundation Model for Spatial Proteomics.

Emerging AI Approaches for Cancer Spatial Omics A Foundation Model for Spatial Proteomics

Reference 44

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This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,.

Emerging AI Approaches for Cancer Spatial Omics Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

Reference 45

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This paper cites Towards Robust Interpretability with Self-Explaining Neural Networks.

Emerging AI Approaches for Cancer Spatial Omics Towards Robust Interpretability with Self-Explaining Neural Networks

Reference 46

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This paper cites Attention is not Explanation,.

Emerging AI Approaches for Cancer Spatial Omics Attention is not Explanation,

Reference 47

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This paper cites Is Attention Interpretable?,.

Emerging AI Approaches for Cancer Spatial Omics Is Attention Interpretable?,

Reference 48

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This paper cites Why Attentions May Not Be Interpretable?.

Emerging AI Approaches for Cancer Spatial Omics Why Attentions May Not Be Interpretable?

Reference 49

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This paper cites A Unified Approach to Interpreting Model Predictions.

Emerging AI Approaches for Cancer Spatial Omics A Unified Approach to Interpreting Model Predictions

Reference 50

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This paper cites "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

Emerging AI Approaches for Cancer Spatial Omics "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Reference 51

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This paper cites Context-guided Responsible Data Augmentation with Diffusion Models.

Emerging AI Approaches for Cancer Spatial Omics Context-guided Responsible Data Augmentation with Diffusion Models

Reference 52

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This paper cites Thermodynamics-inspired explanations of artificial intelligence,.

Emerging AI Approaches for Cancer Spatial Omics Thermodynamics-inspired explanations of artificial intelligence,

Reference 53

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This paper cites Tissue registration and exploration user interfaces in support of a human reference atlas,.

Emerging AI Approaches for Cancer Spatial Omics Tissue registration and exploration user interfaces in support of a human reference atlas,

Reference 54

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This paper cites Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP),.

Emerging AI Approaches for Cancer Spatial Omics Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP),

Reference 55

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correction dated 2024-03-01. Source: crossref record 10.1038/s41556-024-01384-0->10.1038/s41556-023-01194-w:correction, observed 2026-07-11T03:01:20.407983+00:00. This notice travels one citation hop only.

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This paper cites Graph Fourier transform for spatial omics representation and analyses of complex organs,.

Emerging AI Approaches for Cancer Spatial Omics Graph Fourier transform for spatial omics representation and analyses of complex organs,

Reference 56

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Observation 435a5ba1-0089-47b1-aee7-f73d5807cb4e · outbound

This paper cites Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer,.

Emerging AI Approaches for Cancer Spatial Omics Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer,

Reference 57

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.361737Z

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Observation 32963a9d-a39b-4fd2-97d5-0fa878641686 · outbound

This paper cites The single-cell pathology landscape of breast cancer,.

Emerging AI Approaches for Cancer Spatial Omics The single-cell pathology landscape of breast cancer,

Reference 58

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.353166Z

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Observation 0b316e6d-8482-4bb3-b5b6-e8958f6f6d93 · outbound

This paper cites Probing the Limits to Positional Information,.

Emerging AI Approaches for Cancer Spatial Omics Probing the Limits to Positional Information,

Reference 59

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.344738Z

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Observation 5b4ab6cf-b009-4efc-83d9-43178246cfba · outbound

This paper cites Decoding cellular communication: An information theoretic perspective on cytokine and endocrine signaling,.

Emerging AI Approaches for Cancer Spatial Omics Decoding cellular communication: An information theoretic perspective on cytokine and endocrine signaling,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.595490Z

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This paper cites Concepts and Applications of Information Theory to Immuno-Oncology,.

Emerging AI Approaches for Cancer Spatial Omics Concepts and Applications of Information Theory to Immuno-Oncology,

Reference 61

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.336563Z

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Observation 875ed1e4-a418-48ff-9d33-6458cfe55f43 · outbound

This paper cites Integrative spatial analysis reveals a multi-layered organization of glioblastoma,.

Emerging AI Approaches for Cancer Spatial Omics Integrative spatial analysis reveals a multi-layered organization of glioblastoma,

Reference 62

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.328342Z

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Observation 9bbcfd7a-2ceb-4426-90df-79a22ca0969b · outbound

This paper cites The information bottleneck method.

Emerging AI Approaches for Cancer Spatial Omics The information bottleneck method

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.603090Z

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Observation f6093e94-454f-48aa-a459-57e9b8568fce · outbound

This paper cites Identifying maximally informative signal- aware representations of single-cell data using the Information Bottleneck,.

Emerging AI Approaches for Cancer Spatial Omics Identifying maximally informative signal- aware representations of single-cell data using the Information Bottleneck,

Reference 64

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.311971Z

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Observation 07bcbd86-fb96-4d9a-886b-49e35fb51711 · outbound

This paper cites Deep Learning and the Information Bottleneck Principle.

Emerging AI Approaches for Cancer Spatial Omics Deep Learning and the Information Bottleneck Principle

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.608599Z

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Observation 05179b90-e6dc-4252-a94c-92371c30f31a · outbound

This paper cites An Information Theoretic Interpretation to Deep Neural Networks,.

Emerging AI Approaches for Cancer Spatial Omics An Information Theoretic Interpretation to Deep Neural Networks,

Reference 66

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.296153Z

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Observation 0809d4c4-4bdd-4ef9-bacf-53757a196ea0 · outbound

This paper cites DALL·E 3.

Emerging AI Approaches for Cancer Spatial Omics DALL·E 3

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.614085Z

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Observation 13a05c65-933f-4bbd-bdf7-ee31d3f94374 · outbound

This paper cites Stability AI Image Models,.

Emerging AI Approaches for Cancer Spatial Omics Stability AI Image Models,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.616518Z

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Observation 1195b7ae-1ceb-497d-b12d-905d52f57b27 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Emerging AI Approaches for Cancer Spatial Omics Score-Based Generative Modeling through Stochastic Differential Equations

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.618898Z

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Observation 9bb60674-b408-4c7d-8cd1-a5f50dd64b02 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Emerging AI Approaches for Cancer Spatial Omics High-Resolution Image Synthesis with Latent Diffusion Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.621709Z

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Observation 4b5fe832-bdbf-40db-a3b4-474340fe05d8 · outbound

This paper cites Scalable Diffusion Models with Transformers.

Emerging AI Approaches for Cancer Spatial Omics Scalable Diffusion Models with Transformers

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.624510Z

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Observation 0ab5bd42-ee66-4d08-a0ef-a04b1fbf7bb1 · outbound

This paper cites stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics | Briefings in Bioinformatics | Oxford Academic.

Emerging AI Approaches for Cancer Spatial Omics stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics | Briefings in Bioinformatics | Oxford Academic

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.627231Z

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Observation ff849d57-c4a6-4489-97d5-21b10ced37f2 · outbound

This paper cites SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq.

Emerging AI Approaches for Cancer Spatial Omics SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:32:59.269402Z

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Observation 85073df1-06d4-4aa8-b175-d11b29ee291f · outbound

This paper cites DiffuST: a latent diffusion model for spatial transcriptomics denoising,.

Emerging AI Approaches for Cancer Spatial Omics DiffuST: a latent diffusion model for spatial transcriptomics denoising,

Reference 74

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.257360Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e51f5b4e-beda-40f5-abb8-f2017abc9a51 · outbound

This paper cites SpatialDiffusion: Predicting Spatial Transcriptomics with Denoising Diffusion Probabilistic Models,.

Emerging AI Approaches for Cancer Spatial Omics SpatialDiffusion: Predicting Spatial Transcriptomics with Denoising Diffusion Probabilistic Models,

Reference 75

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.249166Z

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Observation 8a2405d6-bdce-4db6-ba2e-b16cb2464f80 · outbound

This paper cites stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation.

Emerging AI Approaches for Cancer Spatial Omics stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:32:59.239888Z

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Observation c4ef83c4-ad4c-4aea-afed-a502c1a62351 · outbound

This paper cites The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability,.

Emerging AI Approaches for Cancer Spatial Omics The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.643457Z

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Observation 83889c91-9a14-414b-ac80-29fadff6270d · outbound

This paper cites Generative diffusion in very large dimensions,.

Emerging AI Approaches for Cancer Spatial Omics Generative diffusion in very large dimensions,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.648780Z

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Observation 286afc8d-2e49-4e66-a5d3-4afb309c3974 · outbound

This paper cites The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability.

Emerging AI Approaches for Cancer Spatial Omics The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.646046Z

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Observation fc9efb8a-e89b-4a4a-9a08-eaf263cb0c3d · outbound

This paper cites Mapping the topography of spatial gene expression with interpretable deep learning,.

Emerging AI Approaches for Cancer Spatial Omics Mapping the topography of spatial gene expression with interpretable deep learning,

Reference 80

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.216752Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c7e605dc-2474-48e3-9d3d-e2d2c30ba88c · outbound

This paper cites A phase transition in diffusion models reveals the hierarchical nature of data,.

Emerging AI Approaches for Cancer Spatial Omics A phase transition in diffusion models reveals the hierarchical nature of data,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.651282Z

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Observation 51d40fa8-2a0f-4531-8689-6f6360d9c4c2 · outbound

This paper cites A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin,.

Emerging AI Approaches for Cancer Spatial Omics A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.658709Z

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Observation 4db2c751-f98f-4546-95e8-ee26230e11f0 · outbound

This paper cites A Reaction-Diffusion Model of Cancer Invasion,.

Emerging AI Approaches for Cancer Spatial Omics A Reaction-Diffusion Model of Cancer Invasion,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.656383Z

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This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Emerging AI Approaches for Cancer Spatial Omics Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.663479Z

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Observation 129c5a2c-8edf-49ab-bcef-a0086b67fd36 · outbound

This paper cites A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth,.

Emerging AI Approaches for Cancer Spatial Omics A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.661012Z

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Observation 9f792490-2ded-4ca2-9b0b-1551d501af30 · outbound

This paper cites NeuroVelo: interpretable learning of temporal cellular dynamics from single-cell data,.

Emerging AI Approaches for Cancer Spatial Omics NeuroVelo: interpretable learning of temporal cellular dynamics from single-cell data,

Reference 86

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.189711Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c44f5b67-686f-408e-84c5-3b9ede7b8f41 · outbound

This paper cites A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data,.

Emerging AI Approaches for Cancer Spatial Omics A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.666627Z

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Observation 8ba13b83-80ba-4a41-9ebb-23fa1b8670ac · outbound

This paper cites SpaCCC: Large language model-based cell-cell communication inference for spatially resolved transcriptomic data,.

Emerging AI Approaches for Cancer Spatial Omics SpaCCC: Large language model-based cell-cell communication inference for spatially resolved transcriptomic data,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.674661Z

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Observation df6b0cca-727d-4c45-a65b-e25f647f3da9 · outbound

This paper cites Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics,.

Emerging AI Approaches for Cancer Spatial Omics Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics,

Reference 89

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.180035Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cbf9609a-629d-4fdf-8d13-df6325c8cfc3 · outbound

This paper cites How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification.

Emerging AI Approaches for Cancer Spatial Omics How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:32:59.164910Z

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Observation 52f53d04-8d5d-425b-ae56-a23e4f828b9a · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Emerging AI Approaches for Cancer Spatial Omics KAN: Kolmogorov-Arnold Networks

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.677177Z

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Observation c9c9baa1-50eb-48dd-980e-cac875639c28 · outbound

This paper cites Mechanical properties of human tumour tissues and their implications for cancer development,.

Emerging AI Approaches for Cancer Spatial Omics Mechanical properties of human tumour tissues and their implications for cancer development,

Reference 92

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.144566Z

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Observation a40d1574-fdf5-429b-8ea6-53c7237f125b · outbound

This paper cites sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling,.

Emerging AI Approaches for Cancer Spatial Omics sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling,

Reference 93

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.153012Z

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Observation 4fd95719-588c-46fe-993b-19e85d960291 · outbound

This paper cites Cell adhesion in cancer: Beyond the migration of single cells,.

Emerging AI Approaches for Cancer Spatial Omics Cell adhesion in cancer: Beyond the migration of single cells,

Reference 94

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.126208Z

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Observation 74d60c4a-53df-4f98-94c3-cf48c51ec489 · outbound

This paper cites Modulating extracellular matrix stiffness: a strategic approach to boost cancer immunotherapy,.

Emerging AI Approaches for Cancer Spatial Omics Modulating extracellular matrix stiffness: a strategic approach to boost cancer immunotherapy,

Reference 95

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.136234Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8a4a5cde-23b2-4b23-a7da-7e78357dc55a · outbound

This paper cites Mechanical Forces in Tumor Angiogenesis,.

Emerging AI Approaches for Cancer Spatial Omics Mechanical Forces in Tumor Angiogenesis,

Reference 96

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.107447Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7aecaee0-e50c-4b2d-a0fc-a9ce4f8a8601 · outbound

This paper cites The physics of cancer: the role of physical interactions and mechanical forces in metastasis,.

Emerging AI Approaches for Cancer Spatial Omics The physics of cancer: the role of physical interactions and mechanical forces in metastasis,

Reference 97

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.116647Z

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Observation 9fa86a7d-f39d-4855-82e0-68850d9ce3e3 · outbound

This paper cites Advances in cancer mechanobiology: Metastasis, mechanics, and materials,.

Emerging AI Approaches for Cancer Spatial Omics Advances in cancer mechanobiology: Metastasis, mechanics, and materials,

Reference 98

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.090206Z

Source-reported events for the cited work

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Observation 0021d107-81fc-4e17-b886-ca8b2e98bc63 · outbound

This paper cites A computational pipeline for spatial mechano-transcriptomics,.

Emerging AI Approaches for Cancer Spatial Omics A computational pipeline for spatial mechano-transcriptomics,

Reference 99

Resolution: verified exact; doi, observed 2026-08-06T21:32:59.098900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52f6cd1c-7359-4d95-af96-4e08b827a7b5 · outbound

This paper cites Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models,.

Emerging AI Approaches for Cancer Spatial Omics Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models,

Reference 100

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