ReviewMolecular bioSystems2013
Analysis of omics data with genome-scale models of metabolism.
Review in Molecular bioSystems, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 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
89 citing papers in PubMed, 194 citations in OpenAlex.
- Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling.Frontiers in bioinformatics · 2026Article
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Longitudinal big biological data in the AI era.Molecular systems biology · 2025Review
- Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions.Nature communications · 2024Article
- Inferring secretory and metabolic pathway activity from omic data with secCellFie.Metabolic engineering · 2024Article
- Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.PNAS nexus · 2024Article
- Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024Review
- Article
- The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation.International journal of molecular sciences · 2023Review
- From observational to actionable: rethinking omics in biologics production.Trends in biotechnology · 2023Review
- A High-Quality Genome-Scale Model forACS synthetic biology · 2023Article
- The importance of understanding the regulation of bacterial metabolism.Environmental microbiology · 2023Article
- Mapping out the gut microbiota-dependent trimethylamine N-oxide super pathway for systems biology applications.Frontiers in systems biology · 2023Review
- INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.PLoS computational biology · 2022Article
- Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication.Computational and structural biotechnology journal · 2022Article
- Phenotypic response of yeast metabolic network to availability of proteinogenic amino acids.Frontiers in molecular biosciences · 2022Article
- ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.PLoS computational biology · 2021Article
- Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines.Biomolecules · 2021Article
29 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 at 1 institution in 1 country.
Funding
Abstract
Over the past decade a massive amount of research has been dedicated to generating omics data to gain insight into a variety of biological phenomena, including cancer, obesity, biofuel production, and infection. Although most of these omics data are available publicly, there is a growing concern that much of these data sit in databases without being used or fully analyzed. Statistical inference methods have been widely applied to gain insight into which genes may influence the activities of others in a given omics data set, however, they do not provide information on the underlying mechanisms or whether the interactions are direct or distal. Biochemically, genetically, and genomically consistent knowledge bases are increasingly being used to extract deeper biological knowledge and understanding from these data sets than possible by inferential methods. This improvement is largely due to knowledge bases providing a validated biological context for interpreting the data.
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.