ReviewCancer informatics2016
Biological Networks for Cancer Candidate Biomarkers Discovery.
Review in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 35 citations in OpenAlex.
- Confounder-Adjusted Differentiation of Colorectal Cancer via Dynamic Propagation of Pathway Influence.International journal of molecular sciences · 2025Article
- Gene regulatory network integration with multi-omics data enhances survival predictions in cancer.Briefings in bioinformatics · 2025Article
- Identification of proteins' expression pathway and the effective miRNAs for the treatment of human papillomavirus-induced cervical cancer: in-silico analyses-experimental research.Annals of medicine and surgery (2012) · 2024Article
- iLSGRN: inference of large-scale gene regulatory networks based on multi-model fusion.Bioinformatics (Oxford, England) · 2023Article
- Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings.Journal of the Egyptian National Cancer Institute · 2023Article
- Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.Frontiers in medicine · 2023Article
- A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins.Journal of personalized medicine · 2022Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- Identification ofCell journal · 2021Article
- Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease.Frontiers in genetics · 2021Review
- Computational Approaches in Theranostics: Mining and Predicting Cancer Data.Pharmaceutics · 2019Review
- Improved annotation ofPeerJ · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.
Indexed as
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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.