Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data
| Autor: | Thangamani Murugesan, Prasanna Vasudevan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
sparse reduced-rank regression Cancer Research Computer science Gene Expression Genomics Computational biology Silhouette genome scale data 03 medical and health sciences 0302 clinical medicine Biomarkers Tumor Cluster Analysis Humans prognosis-enhanced neural network Cluster analysis classifier Clustering coefficient Brain Neoplasms Genome Human Gene Expression Profiling Omics Prognosis graph clustering Gene expression profiling Gene Expression Regulation Neoplastic 030104 developmental biology Oncology 030220 oncology & carcinogenesis cancer subtypes Human genome Original Article Neural Networks Computer Nerve Net Glioblastoma Classifier (UML) Algorithms multidimensional data |
| Zdroj: | Technology in Cancer Research & Treatment |
| ISSN: | 1533-0338 1533-0346 |
| Popis: | Objective The 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. Methods Initially, 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. Results Max-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. Conclusion From 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. |
| Databáze: | OpenAIRE |
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