ReviewJournal of Cancer2015
Pathway and network approaches for identification of cancer signature markers from omics data.
Review in Journal of Cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Age-related uterine changes and its association with poor reproductive outcomes: a systematic review and meta-analysis.Reproductive biology and endocrinology : RB&E · 2024Pooled it
- Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.Genes & genomics · 2024Article
- Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data.Entropy (Basel, Switzerland) · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- LAMP: disease classification derived from layered assessment on modules and pathways in the human gene network.BMC bioinformatics · 2020Article
- An Effective Graph Clustering Method to Identify Cancer Driver Modules.Frontiers in bioengineering and biotechnology · 2020Article
- Integrative Analysis of Breast Cancer Cells Reveals an Epithelial-Mesenchymal Transition Role in Adaptation to Acidic Microenvironment.Frontiers in oncology · 2020Article
- Characterization and Validation of an "Acute Aerobic Exercise Load" as a Tool to Assess Antioxidative and Anti-inflammatory Nutrition in Healthy Subjects Using a Statistically Integrated Approach in a Comprehensive Clinical Trial.Oxidative medicine and cellular longevity · 2019Article
- Genome-wide expression profiling of glioblastoma using a large combined cohort.Scientific reports · 2018Article
- The three-legged stool of understanding metabolism: integrating metabolomics with biochemical genetics and computational modeling.AIMS microbiology · 2018Article
- Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma.Molecular oncology · 2018Article
- Signalling maps in cancer research: construction and data analysis.Database : the journal of biological databases and curation · 2018Article
- Metabolomic profiling of breast tumors using ductal fluid.International journal of oncology · 2016Article
- A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.Scientific reports · 2016Article
- Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples.Molecular biology reports · 2016Article
- Network-Based Protein Biomarker Discovery Platforms.Genomics & informatics · 2016Review
- Functional divergence and convergence between the transcript network and gene network in lung adenocarcinoma.OncoTargets and therapy · 2016Article
- Biological Networks for Cancer Candidate Biomarkers Discovery.Cancer informatics · 2016Review
- Genomic and Transcriptomic Alterations Associated with STAT3 Activation in Head and Neck Cancer.PloS one · 2016Article
- Genomic, Proteomic, and Metabolomic Data Integration Strategies.Biomarker insights · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.
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.