Aggregating multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal
Kirchgaessner, R.; Keutler, K.; Sivakumar, L.; Song, X.; Ellrott, K.
Show abstract
AI based embeddings offer the possibilities of encoding complex biological data into low dimensional spaces, called embedding spaces, that maintain the relationships between entities. There is an open question about the compatibility of embedding spaces that are created without any coordination. It has been assumed that signals in these unaligned embedding spaces would be destroyed if vectors were aggregated into summed values. We trained embedding models across different data modalities and tested aggregating the values together to test this assumption. Our research shows that signal from unaligned embedded values is conserved and able to still be used for learning tasks, such as data modality and tumor of origin recognition.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- COSIME: Cooperative multi-view integration with Scalable and Interpretable Model Explainer 94%
- Multi-V-Stain: Multiplexed Virtual Staining of Histopathology Whole-Slide Images 93%
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer 93%
Similar papers in this journal
- Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis 95%
- Spatial Transcriptomics Inferred from Pathology Whole-Slide Images Links Tumor Heterogeneity to Survival in Breast and Lung Cancer 95%
- Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology 94%
Similar papers in this journal
- Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference 94%
- Single-Cell Multi-Modal GAN (scMMGAN) reveals spatial patterns in single-cell data from triple negative breast cancer 94%
- Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study 94%
Similar papers in this journal
- Features fusion or not: harnessing multiple pathological foundation models using Meta-Encoder for downstream tasks fine-tuning 95%
- Deep transfer learning for reducing health care disparities arising from biomedical data inequality 94%
- MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification 94%
Similar papers in this journal
- Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge 94%
- A curriculum learning approach to training antibody language models 94%
- A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.