ArticleTechnology in cancer research & treatment2018
Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data.
Article in Technology in cancer research & treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data.GigaScience · 2026Article
- Lung cancer diagnosis based on weighted convolutional neural network using gene data expression.Scientific reports · 2024Article
- Time-series data prediction problem analysis through multilayered intuitionistic fuzzy sets.Soft computing · 2023Article
- Profiling hearing aid users through big data explainable artificial intelligence techniques.Frontiers in neurology · 2022Article
- Prediction of steroid resistance and steroid dependence in nephrotic syndrome children.Journal of translational medicine · 2021Article
- Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma.Journal of magnetic resonance imaging : JMRI · 2020Review
- Landscape of active enhancers developed de novo in cirrhosis and conserved in hepatocellular carcinoma.American journal of cancer research · 2020Article
- Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology.Frontiers in pharmacology · 2020Review
- Integrated Cancer Subtyping using Heterogeneous Genome-Scale Molecular Datasets.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine.Frontiers in oncology · 2019Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe main objective in studying large-scale cancer omics is to identify molecular mechanisms of cancer and discover novel biomedical targets. This work not only discovers the cancer subtypes in genome scale data by using clustering and classification but also measures their accuracy.
methodsInitially, candidate cancer subtypes are recognized by max-flow/min-cut graph clustering. Finally, prognosis-enhanced neural network classifier is proposed for classification. We analyzed the heterogeneity and identified the subtypes of glioblastoma multiforme, an aggressive adult brain tumor, from 215 samples with microRNA expression (12 042 genes). The samples were classified into 4 different classes such as mesenchymal, classical, proneural, and neural subtypes owing to mutations and gene expression. The results are measured using the metrics such as silhouette width, biological stability index, clustering accuracy, precision, recall, and f-measure.
resultsMax-flow/min-cut clustering produces higher clustering accuracy of 88.93% for 215 samples. The proposed prognosis-enhanced neural network classifier algorithm produces higher accuracy results of 89.2% for 215 samples efficiently.
conclusionFrom the experimental results, the proposed prognosis-enhanced neural network classifier is seen as an alternative, which is full of promise for cancer subtype prediction in genome scale data.
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.