ArticleBMC bioinformatics2014
Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientist's point of view.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.Analytica chimica acta · 2026Review
- Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Mapping current research status and emerging frontiers of lipidomics: a comprehensive data-mining-based study.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Identification of pre-diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case-control study within the ATBC cohort.International journal of cancer · 2024Article
- Metabolites in the regulatory risk assessment of pesticides in the EU.Frontiers in toxicology · 2023Review
- Multi-Omics Analysis Revealed a Significant Alteration of Critical Metabolic Pathways Due to Sorafenib-Resistance in Hep3B Cell Lines.International journal of molecular sciences · 2022Article
- Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra.Analytical and bioanalytical chemistry · 2022Article
- Influence of Nutritional Status and Physical Exercise on Immune Response in Metabolic Syndrome.Nutrients · 2022Review
- Approaches to Heterogeneity in Native Mass Spectrometry.Chemical reviews · 2022Review
- Integrative proteomics and metabolomics of Guizhou Miao Sour Soup affecting simple obese rats.Frontiers in nutrition · 2022Article
- Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics.Computational and structural biotechnology journal · 2022Review
- Automated data-driven mass spectrometry for improved analysis of lipids with dual dissociation techniques.Journal of mass spectrometry and advances in the clinical lab · 2021Article
- MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization.Metabolites · 2021Article
- Integrative proteomic and lipidomic analysis of Kaili Sour Soup-mediated attenuation of high-fat diet-induced nonalcoholic fatty liver disease in a rat model.Nutrition & metabolism · 2021Article
- Software Options for the Analysis of MS-Proteomic Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona.Nature communications · 2020Observational
- EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution.Analytical chemistry · 2020Article
- Comparative Proteomic Analysis of Nodulated and Non-NodulatedInternational journal of molecular sciences · 2019Article
- In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics.Metabolites · 2019Article
- Variable selection and validation in multivariate modelling.Bioinformatics (Oxford, England) · 2019Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult.
resultsThis paper provides an introduction to the burgeoning field of computational mass spectrometry. We illustrate key concepts, vocabulary, and open problems in MS-omics, as well as provide invaluable resources such as open data sets and key search terms and references.
conclusionsThis paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform novel algorithmic solutions to open problems.
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.