ArticleGenome biology2023
CMOT: Cross-Modality Optimal Transport for multimodal inference.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer's disease.Nature communications · 2026Article
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.Nature communications · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
- TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.Genome biology · 2024Article
- MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology.Patterns (New York, N.Y.) · 2023Article
- CMOT: Cross-Modality Optimal Transport for multimodal inference.Genome biology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell-cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.