ReviewFrontiers in genetics2019
Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed.
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Water kefir as a paradigm for multi-omics and genome-scale metabolic modelling in fermented food.NPJ biofilms and microbiomes · 2026Review
- Pharmaceutical Strategies for West Nile Virus in Europe, an Underrecognized Cause of Severe Disease and Mortality in Older Adults: From Supportive Care to Antiviral Development.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Challenges and Opportunities in Multi-Omics Data Acquisition and Analysis: Toward Integrative Solutions.Biomolecules · 2026Review
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- Reframing brain aging: neuroinflammation as an interconnected network process.Frontiers in aging · 2026Review
- Remics: a redescription-based framework for multi-omics analysis.Frontiers in cell and developmental biology · 2026Article
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Risk factors for fluoroquinolone- and macrolide-resistance among swine Campylobacter coli using multi-layered chain graphs.PLoS computational biology · 2025Article
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.Computational and structural biotechnology journal · 2025Review
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- MARML: Motif-Aware Deep Representation Learning in Multilayer Networks.IEEE transactions on neural networks and learning systems · 2024Article
- Comparative analysis of integrative classification methods for multi-omics data.Briefings in bioinformatics · 2024Article
- A multimodal graph neural network framework for cancer molecular subtype classification.BMC bioinformatics · 2024Article
- Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).GigaScience · 2024Article
- Integrative approach of omics and imaging data to discover new insights for understanding brain diseases.Brain communications · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.
Indexed as
Identifiers
What OpenQuestion holds
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.