ArticleScience (New York, N.Y.)2018
Realizing private and practical pharmacological collaboration.
Article in Science (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.Proceedings. UNIX Security Symposium · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study.NPJ digital medicine · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Advancements in brain-machine interfaces for application in the metaverse.Frontiers in neuroscience · 2024Review
- Recent advances in anti-inflammatory active components and action mechanisms of natural medicines.Inflammopharmacology · 2023Review
- Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Distribution-Invariant Differential Privacy.Journal of econometrics · 2023Article
- Review
- Contrastive learning in protein language space predicts interactions between drugs and protein targets.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing.Genome biology · 2023Article
- EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.BMC bioinformatics · 2022Article
- The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Motive perception pathways to the release of personal information to healthcare organizations.BMC medical informatics and decision making · 2022Article
- Privacy-preserving federated neural network learning for disease-associated cell classification.Patterns (New York, N.Y.) · 2022Article
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
- Privacy-preserving genotype imputation with fully homomorphic encryption.Cell systems · 2022Article
- A guide to machine learning for biologists.Nature reviews. Molecular cell biology · 2022Review
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
Although combining data from multiple entities could power life-saving breakthroughs, open sharing of pharmacological data is generally not viable because of data privacy and intellectual property concerns. To this end, we leverage modern cryptographic tools to introduce a computational protocol for securely training a predictive model of drug-target interactions (DTIs) on a pooled dataset that overcomes barriers to data sharing by provably ensuring the confidentiality of all underlying drugs, targets, and observed interactions. Our protocol runs within days on a real dataset of more than 1 million interactions and is more accurate than state-of-the-art DTI prediction methods. Using our protocol, we discover previously unidentified DTIs that we experimentally validated via targeted assays. Our work lays a foundation for more effective and cooperative biomedical research.
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.