ArticleChemical communications (Cambridge, England)2019
Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.
Article in Chemical communications (Cambridge, England), 2019. 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.
- Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.Journal of food and drug analysis · 2026Review
- Metabolomics of healthy hematopoietic stem cells and leukemic stem cells.Journal of clinical and translational research · 2025Article
- Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage.Journal of visualized experiments : JoVE · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Developing a Cell Quenching Method to Facilitate Single Cell Mass Spectrometry Metabolomics Studies.JACS Au · 2025Article
- Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions.Analytical chemistry · 2025Review
- MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.Analytical chemistry · 2024Article
- MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method.Analytica chimica acta · 2024Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?Cancers · 2024Review
- Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics.Analytical chemistry · 2023Article
- Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level.Frontiers in network physiology · 2023Article
- Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease.Analytical chemistry · 2022Article
- AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.Cell reports. Physical science · 2022Article
- Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty.Scientific reports · 2022Article
- Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery.Cancer biomarkers : section A of Disease markers · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Single cell metabolomics using mass spectrometry: Techniques and data analysis.Analytica chimica acta · 2021Review
- Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells.Frontiers in genetics · 2021Review
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
Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation. This approach can be potentially applied towards prediction of drug-resistant phenotypes prior to chemotherapy.
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.