ArticleMetabolomics : Official journal of the Metabolomic Society2011
Bioinformatics tools for cancer metabolomics.
Article in Metabolomics : Official journal of the Metabolomic Society, 2011. 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.
- Article
- Metabolomics in cancer detection: A review of techniques, biomarkers, and clinical utility.BioMedicine · 2025Review
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.Frontiers in molecular biosciences · 2024Review
- asmbPLS: biomarker identification and patient survival prediction with multi-omics data.Frontiers in genetics · 2024Article
- To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.Signal transduction and targeted therapy · 2023Review
- Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma.Diagnostics (Basel, Switzerland) · 2023Review
- KODAMA exploratory analysis in metabolic phenotyping.Frontiers in molecular biosciences · 2022Review
- Review
- Salivary Metabolomics for Diagnosis and Monitoring Diseases: Challenges and Possibilities.Metabolites · 2021Review
- Article
- Lipidomics investigations into the tissue phospholipidomic landscape of invasive ductal carcinoma of the breast.RSC advances · 2020Article
- Metabolomics technology and bioinformatics for precision medicine.Briefings in bioinformatics · 2019Review
- Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions.Current nutrition reports · 2019Review
- Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach.Current rheumatology reports · 2019Review
- Combined metabolic, phenomic and genomic data to prioritize atrial fibrillation-related metabolites.Experimental and therapeutic medicine · 2019Article
- Identification of Key Metabolites for Acute Lung Injury in Patients with Sepsis.Iranian journal of public health · 2019Article
- Low-Field, Benchtop NMR Spectroscopy as a Potential Tool for Point-of-Care Diagnostics of Metabolic Conditions: Validation, Protocols and Computational Models.High-throughput · 2018Article
- A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation.Metabolites · 2018Article
- Integrating transcriptomic techniques and k-means clustering in metabolomics to identify markers of abiotic and biotic stress in Medicago truncatula.Metabolomics : Official journal of the Metabolomic Society · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.
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.