ArticleBriefings in bioinformatics2025
Early cancer detection via multi-omics cfDNA fragmentation using early-late fusion neural network with sample-modality evaluation.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cell-Free DNA Fragmentation Patterns as Biomarkers for Human Papillomavirus-Related Cancers: A Systematic Review of Methodological Diversity and Diagnostic Performance.International journal of molecular sciences · 2026Pooled it
- MOCDT: multi-cancer detection and tissue-of-origin classification via cfDNA multi-modal integration.Bioinformatics (Oxford, England) · 2026Article
- Early Cancer Detection: What's Going on and What's Next.MedComm · 2026Review
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Authors and funding
3 authors.
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Abstract
Cell-free DNA (cfDNA) fragmentation patterns reflect epigenetic modifications and are promising biomarkers for early cancer detection. While integrating diverse fragmentomic signals can improve accuracy, high modality dimensionality, and limited samples challenge effective multimodal fusion. We present Early-Late fusion with Sample-Modality evaluation (ELSM), a two-stage neural network integrating 13 fragmentomic feature spaces with sample-wise modality evaluation to capture complementary signals. Across five datasets of 1994 samples from 10 cancer types, ELSM outperforms unimodal and advanced multimodal models for cancer detection and tissue-of-origin prediction, achieving an AUC of 0.972 for pan-cancer diagnosis and 0.922 in an independent gastric cancer cohort, with a median tissue-of-origin accuracy of 0.683. Analysis of key genomic regions identified by ELSM reveals robust interpretability aligned with known oncogenic pathways. ELSM provides a powerful and interpretable framework for integrative multi-omics analysis with strong potential for clinical translation in early cancer detection.
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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 Socratic graph.