ArticleCell genomics2024
AI-empowered perturbation proteomics for complex biological systems.
Article in Cell genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
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- AI proteomics: from protein identification to virtual cells.Nature methods · 2026Review
- CRISPR-Cas9 and precision editing technologies linking functional genomics to clinical translation in genetic diseases.Clinical and translational medicine · 2026Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Biophysical Sensing Tools in Drug Discovery: Integrating Kinetics, Thermodynamics, Cellular Target Engagement and Structure.Sensors (Basel, Switzerland) · 2026Review
- Review
- Explorative Insights into Local Immune Response to BK Virus-A Cross-Sectional Study in Urine Samples Between Transplant Recipients and Non-Immunocompromised Hosts.Medicina (Kaunas, Lithuania) · 2026Article
- Therapeutic target database 2026: facilitating targeted therapies and precision medicine.Nucleic acids research · 2026Article
- Liquid Biopsy and Multi-Omic Biomarkers in Breast Cancer: Innovations in Early Detection, Therapy Guidance, and Disease Monitoring.Biomedicines · 2025Review
- Will AI become our Co-PI?NPJ digital medicine · 2025Review
- Review
- Grow AI virtual cells: three data pillars and closed-loop learning.Cell research · 2025Article
- Review
- Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.Frontiers in genetics · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.
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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 Socratic graph.