ArticleNucleic acids research2017
Individual-specific edge-network analysis for disease prediction.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- MicroSSNet: an R package for microbial network construction and analysis at the single-sample and aggregated levels.BMC bioinformatics · 2026Article
- Detecting the Pre-Disease State of Single Sample Through the Change in Local Network Enrichment Level.Genes · 2026Article
- Osteopenia Metabolomic Biomarkers for Early Warning of Osteoporosis.Metabolites · 2025Article
- Disease prediction by network information gain on a single sample basis.Fundamental research · 2025Article
- A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.Human genetics · 2024Article
- Integrating multi-omics data of childhood asthma using a deep association model.Fundamental research · 2024Article
- Using single-sample networks to identify the contrasting patterns of gene interactions and reveal the radiation dose-dependent effects in multiple tissues of spaceflight mice.NPJ microgravity · 2024Article
- Multi-modal molecular determinants of clinically relevant osteoporosis subtypes.Cell discovery · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Individual-network based predictions of microbial interaction signatures for response to biological therapies in IBD patients.Frontiers in molecular biosciences · 2024Article
- Gene expression ranking change based single sample pre-disease state detection.Frontiers in genetics · 2024Article
- Edge and modular significance assessment in individual-specific networks.Scientific reports · 2023Article
- Capturing the dynamics of microbial interactions through individual-specific networks.Frontiers in microbiology · 2023Article
- The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression.Journal of molecular cell biology · 2022Article
- Identification and Validation of TRIM25 as a Glucose Metabolism Regulator in Prostate Cancer.International journal of molecular sciences · 2022Article
- Noninvasive detection and interpretation of gastrointestinal diseases by collaborative serum metabolite and magnetically controlled capsule endoscopy.Computational and structural biotechnology journal · 2022Article
- Network-based cancer genomic data integration for pattern discovery.BMC genomic data · 2021Article
- Identification of Immune Function-Related Subtypes in Cutaneous Melanoma.Life (Basel, Switzerland) · 2021Article
- Facial Skin Microbiota-Mediated Host Response to Pollution Stress Revealed by Microbiome Networks of Individual.mSystems · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting pre-disease state or tipping point just before irreversible deterioration of health is a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory opens a new way to study this problem by exploring rich dynamical and high-dimensional information of omics data. Although theoretically ENA has the ability to identify the pre-disease state during the disease progression, it requires multiple samples for such prediction on each individual, which are generally not available in clinical practice, thus limiting its applications in personalized medicine. In this work to overcome this problem, we propose the individual-specific ENA (iENA) with DNB to identify the pre-disease state of each individual in a single-sample manner. In particular, iENA can identify individual-specific biomarkers for the disease prediction, in addition to the traditional disease diagnosis. To demonstrate the effectiveness, iENA was applied to the analysis on omics data of H3N2 cohorts and successfully detected early-warning signals of the influenza infection for each individual both on the occurred time and event in an accurate manner, which actually achieves the AUC larger than 0.9. iENA not only found the new individual-specific biomarkers but also recovered the common biomarkers of influenza infection reported from previous works. In addition, iENA also detected the critical stages of multiple cancers with significant edge-biomarkers, which were further validated by survival analysis on both TCGA data and other independent data.
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