ReviewNature reviews. Genetics2013
Computational solutions for omics data.
Review in Nature reviews. Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 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
132 citing papers in PubMed.
- engGNN: a dual-graph neural network for omics-based disease classification and feature selection.Briefings in bioinformatics · 2026Article
- DNA framework-based molecular transformer for logic-driven precision diagnostics.Science advances · 2026Article
- engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection.ArXiv · 2026Article
- Patient stratification reveals the molecular basis of disease co-occurrences.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Stacked Deep Learning Ensemble for Multiomics Cancer Type Classification: Development and Validation Study.JMIR bioinformatics and biotechnology · 2025Article
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Cardiac Fibrosis in the Multi-Omics Era: Implications for Heart Failure.Circulation research · 2025Review
- Molecular Mechanisms of Ischemia/Reperfusion Injury and Graft Dysfunction in Liver Transplantation: Insights from Multi-Omics Studies in Rodent Animal Models.International journal of biological sciences · 2025Review
- Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection.Briefings in bioinformatics · 2024Article
- Review
- CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.Nature communications · 2024Article
- A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions.Nature communications · 2024Article
- Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.Life (Basel, Switzerland) · 2024Review
- Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont.FEMS microbiology ecology · 2024Review
- Retrieval of long DNA reads from herbarium specimens.AoB PLANTS · 2023Review
- Article
- Multi-Omics Approaches to Improve Meat Quality and Taste Characteristics.Food science of animal resources · 2023Review
- Uncertainty-aware dynamic integration for multi-omics classification of tumors.Journal of cancer research and clinical oncology · 2023Article
- Navigating bottlenecks and trade-offs in genomic data analysis.Nature reviews. Genetics · 2023Review
- Multi-scale models of whole cells: progress and challenges.Frontiers in cell and developmental biology · 2023Review
72 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
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.