ArticleInternational journal of molecular sciences2019
High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Gastroesophageal disease risk and inhalational exposure a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Identification of immunosuppressive neutrophils using multi-omics: why functional testing remains key.Frontiers in immunology · 2026Review
- Deciphering the Regulatory Networks of the Migrasome-Associated Cell Subpopulation in Heterotopic Ossification via Multi-Omics Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Prediction of Acute Kidney Injury for Critically Ill Cardiogenic Shock Patients with Machine Learning Algorithms.International journal of general medicine · 2025Article
- Advances in the diagnosis of colorectal cancer: the application of molecular biomarkers and imaging techniques: a literature review.Annals of medicine and surgery (2012) · 2025Review
- The Role of NOL3 in Colon Adenocarcinoma Metastasis and Its Association With DNA Methylation.Human mutation · 2025Article
- Unveiling CD177: a key player in tumors, autoimmune diseases, and inflammatory disorders.Frontiers in immunology · 2025Review
- Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.NAR genomics and bioinformatics · 2024Article
- Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN) in World Trade Center exposed firefighters: a case-control observational study protocol.BMC gastroenterology · 2024Article
- Enhancing selection of alcohol consumption-associated genes by random forest.The British journal of nutrition · 2024Article
- Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN): a Case-Control Observational Study Protocol.Research square · 2024Article
- Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.The Journal of international medical research · 2023Article
- Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation.Scientific reports · 2023Article
- CpG Site-Based Signature Predicts Survival of Colorectal Cancer.Biomedicines · 2022Article
- Influence of S100A2 in Human Diseases.Diagnostics (Basel, Switzerland) · 2022Review
- Targeting metabolism: A potential strategy for hematological cancer therapy.World journal of clinical cases · 2022Review
- Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models.Laboratory investigation; a journal of technical methods and pathology · 2022Article
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in
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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.