ArticleCell reports methods2024
Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Redefining druggable targets with artificial intelligence.Nature biotechnology · 2025Article
- The evolution of analytical techniques for multiplex analysis of protein biomarkers.Expert review of proteomics · 2025Review
- The Role of Proteomics and Genomics in the Development of Colorectal Cancer Diagnostic Tools and Potential New Treatments.ACS pharmacology & translational science · 2025Review
- sc2DAT: workflow for targeting tumor subpopulations of single cells.Bioinformatics advances · 2025Article
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
The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.
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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.