ArticleExploratory research and hypothesis in medicine
Advances in the Clinical Application of High-throughput Proteomics.
Article in Exploratory research and hypothesis in medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Proteomic Mediators Linking Autoimmune Diseases to Major Adverse Cardiovascular Events: Insights from the UK Biobank.Proteomes · 2026Article
- Inflammatory-neurodegenerative crosstalk in pediatric severe traumatic brain injury: a multi-domain plasma proteomic study.Molecular medicine (Cambridge, Mass.) · 2026Article
- Development of a multiplex assay for early detection of pancreatic cancer in high-risk groups.BMC cancer · 2026Article
- Associations of Normalization and Regularization with Machine Learning Overfitting in Cross-dataset Classification of Deaths Using Transcriptomic and Clinical Data: A Secondary Analysis of Publicly Available Databases.Journal of clinical and translational pathology · 2026Article
- Peptide Arrays as Tools for Unraveling Tumor Microenvironments and Drug Discovery in Oncology.Cells · 2026Review
- Editorial for Special Issue "Technological Advances Around Next-Generation Sequencing".Current issues in molecular biology · 2026Article
- Screening of serum biomarkers for coronary artery calcification using DIA quantitative proteomics and construction of a regression model.Frontiers in cardiovascular medicine · 2026Article
- Introduction to the Immune System.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Identification of serum proteins associated with response of triple-negative breast cancer to neoadjuvant chemotherapy: preliminary results from the INSTIGO trial.Clinical proteomics · 2025Article
- Placental proteomic signatures of preterm birth, gestational age, and birthweight.BMC pregnancy and childbirth · 2025Article
- Tonic signaling of the B-cell antigen-specific receptor is a common functional hallmark in chronic lymphocytic leukemia cell phosphoproteomes at early disease stages.Molecular oncology · 2025Article
- Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies.Genes & nutrition · 2025Review
- Proteomic Perspectives on KRAS-Driven Cancers and Emerging Therapeutic Approaches.Current oncology (Toronto, Ont.) · 2025Review
- Next-generation leukemia diagnostics: Integrating LC-MS/MS proteomics with liquid biopsy platforms.The journal of liquid biopsy · 2025Review
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
- Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory - A Mini-review on the Benefits and Pitfalls of Its Implementation.Journal of clinical and translational pathology · 2025Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.
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.