ReviewBriefings in bioinformatics2019
Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Enhancing validation of case-control omics signatures through "minimalist" single-subject analysis (N-of-1 trials): proof of concept in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Alternative Splicing of Serum Response Factor Reveals Isoform-Specific Remodeling in Cardiac Diseases.Genes · 2025Article
- Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold.International journal of molecular sciences · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology.International journal of molecular sciences · 2023Review
- How CD4Biomedicines · 2023Article
- Epithelial cell responses to rhinovirus identify an early-life-onset asthma phenotype in adults.The Journal of allergy and clinical immunology · 2022Article
- Article
- Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.Briefings in bioinformatics · 2021Article
- 'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases.Bioinformatics (Oxford, England) · 2021Article
- Article
- Personalized beyond Precision: Designing Unbiased Gold Standards to Improve Single-Subject Studies of Personal Genome Dynamics from Gene Products.Journal of personalized medicine · 2020Article
- Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling.Cancers · 2020Article
- PenDA, a rank-based method for personalized differential analysis: Application to lung cancer.PLoS computational biology · 2020Article
- Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.Frontiers in genetics · 2020Article
- Knowledge Generation with Rule Induction in Cancer Omics.International journal of molecular sciences · 2019Review
- Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine.BMC medical genomics · 2019Article
- Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- N-of-1 Clinical Trials in Nutritional Interventions Directed at Improving Cognitive Function.Frontiers in nutrition · 2019Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.
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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.