ArticleNPJ digital medicine2022
Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Review
- Interpretable Differential Abundance Signature (iDAS).Small methods · 2026Article
- A Bayesian Informative Shrinkage Approach for Large-scale Multiple Hypothesis Testing (BISHOT): with Applications in Differential Analysis of Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies.Genes & nutrition · 2025Review
- A message passing framework for precise cell state identification with scClassify2.Genome biology · 2025Article
- A flexible framework for minimal biomarker signature discovery from clinical omics studies without library size normalisation.PLOS digital health · 2025Article
- Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas.Nature medicine · 2024Article
- Construction and optimization of multi-platform precision pathways for precision medicine.Scientific reports · 2024Article
- Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.
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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.