ArticleOmics : a journal of integrative biology2011
Biomarkers in the age of omics: time for a systems biology approach.
Article in Omics : a journal of integrative biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- Prediction of trajectories and outcomes in early-stage metabolic dysfunction-associated steatotic liver disease: a narrative review.EClinicalMedicine · 2026Review
- Peripheral blood tumor marker levels can indicate the location of lung cancer metastasis.Oncology letters · 2025Article
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Harnessing brain-derived extracellular vesicles to support RDoC-based drug development.Neuroscience applied · 2025Review
- Review
- Climate change and cetacean health: impacts and future directions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Precision medicine: Concept and tools.Medical journal, Armed Forces India · 2021Review
- Metabolomic Biomarkers in Anxiety Disorders.International journal of molecular sciences · 2020Review
- Distinct pattern of one-carbon metabolism, a nutrient-sensitive pathway, in invasive breast cancer: A metabolomic study.Oncotarget · 2020Article
- Metabolomics analysis of the hippocampus in a rat model of traumatic brain injury during the acute phase.Brain and behavior · 2020Article
- Omics-Driven Biomarkers of Psoriasis: Recent Insights, Current Challenges, and Future Prospects.Clinical, cosmetic and investigational dermatology · 2020Review
- Differentiating Psychosomatic, Somatopsychic, Multisystem Illnesses, and Medical Uncertainty.Healthcare (Basel, Switzerland) · 2019Review
- How to Eliminate Uncertainty in Clinical Medicine - Clues from Creation of Mathematical Models Followed by Scientific Data Mining.EBioMedicine · 2018Article
- Mechanism-Based Disease Progression Model Describing Host-Pathogen Interactions During the Pathogenesis of Acinetobacter baumannii Pneumonia.CPT: pharmacometrics & systems pharmacology · 2018Article
- A multi-omic analysis reveals the regulatory role of CD180 during the response of macrophages to Borrelia burgdorferi.Emerging microbes & infections · 2018Article
- Metabolomic alterations in invasive ductal carcinoma of breast: A comprehensive metabolomic study using tissue and serum samples.Oncotarget · 2018Article
- PhyloOncology: Understanding cancer through phylogenetic analysis.Biochimica et biophysica acta. Reviews on cancer · 2017Review
- Structural and functional alterations in the colonic microbiome of the rat in a model of stress induced irritable bowel syndrome.Gut microbes · 2017Article
- Genomics of Colorectal Cancer in African Americans.Next generation, sequencing & applications · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Limitations to biomarker discovery are not only technical or bioinformatic but conceptual as well. In our attempt to offer a solution, we are highlighting three issues that we think are limiting progress in biomarkers discovery. First, the confusion stemming from the imposition of a pathology-type immunohistochemical marker (IHCM) concept on omics data without fully understanding the characteristics and limitations of IHCMs as applied in clinical pathology. Second, the lack of serious consideration for the scope of disease heterogeneity. Third, the refusal of the biomedical community to borrow from other biological disciplines their well established methods for dealing with heterogeneity. Therefore, real progress in biomarker discovery will be attained when we recognize that an omics biomarker cannot be assigned and validated without a priori data modeling and subtyping of the disease itself to reveal the extent of its heterogeneity, and its omics' clonal aberrations (drivers) underlying its subtypes and pathways' diversity. To further support our viewpoints, we are contributing a novel a systems biology method such as parsimony phylogenetic approach for disease modeling prior to biomarker circumscription. As an analytical approach that has been successfully used for a half of a century in other biological disciplines, parsimony phylogenetics simultaneously achieves several objectives: it provides disease modeling in a hierarchical phylogenetic classification, identifies biomarkers as the shared derived expressions or mutations--synapomorphies, constructs the omics profiles of specimens based on the most parsimonious arrangement of their heterogeneous data, and permits network profiling of affected signaling pathways as the biosignature of disease classes.
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.