ReviewCancer informatics2014
Network-based identification of biomarkers coexpressed with multiple pathways.
Review in Cancer informatics, 2014. 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, 38 citations in OpenAlex.
- Pan-cancer analysis for the prognostic and immunological role of CD47: interact with TNFRSF9 inducing CD8 + T cell exhaustion.Discover oncology · 2024Article
- Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema.American journal of respiratory and critical care medicine · 2024Article
- Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package.Scientific reports · 2023Article
- "iNETgrate": integrating DNA methylation and gene expression data in a single gene network.Research square · 2023Article
- Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.TheScientificWorldJournal · 2023Article
- Review
- Computational Tactics for Precision Cancer Network Biology.International journal of molecular sciences · 2022Review
- Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma.Frontiers in pharmacology · 2022Article
- Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders.Applied biochemistry and biotechnology · 2022Article
- Immune-Omics Networks ofCancers · 2021Article
- Understanding the Determination of Meat Quality Using Biochemical Characteristics of the Muscle: Stress at Slaughter and Other Missing Keys.Foods (Basel, Switzerland) · 2021Review
- Identifying anti-TNF response biomarkers in ulcerative colitis using a diffusion-based signalling model.Bioinformatics advances · 2021Article
- Integrative Analysis of Breast Cancer Cells Reveals an Epithelial-Mesenchymal Transition Role in Adaptation to Acidic Microenvironment.Frontiers in oncology · 2020Article
- Topological properties and in vitro identification of essential nodes of the Paclitaxel and Vincristine interactomes in PC-3 cells.Biomedical journal · 2019Article
- Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.Frontiers in genetics · 2018Article
- Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.Big data · 2016Article
- Biological Networks for Cancer Candidate Biomarkers Discovery.Cancer informatics · 2016Review
- Systematic pan-cancer analysis of tumour purity.Nature communications · 2015Article
- Evidence of perturbations of the cytokine network in preterm labor.American journal of obstetrics and gynecology · 2015Article
- Network and pathway analysis of cancer susceptibility (a).Cancer informatics · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.
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.