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Improved cancer biomarkers identification using network-constrained infinite latent feature selection
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 2, p e0246668 (2021)
- Publication Year :
- 2020
-
Abstract
- Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.
- Subjects :
- Male
Support Vector Machine
Computer science
Biochemistry
Infographics
Discriminative model
Neoplasms
Gene expression
Breast Tumors
Medicine and Health Sciences
Gene Regulatory Networks
Oligonucleotide Array Sequence Analysis
Data Management
Multidisciplinary
Liver Diseases
Prostate Cancer
Prostate Diseases
Identification (information)
Oncology
Nephrology
Renal Cancer
Medicine
Female
Graphs
Algorithms
Research Article
Computer and Information Sciences
Science
Urology
Feature selection
Computational biology
Gastroenterology and Hepatology
Set (abstract data type)
Gastrointestinal Tumors
Breast Cancer
Biomarkers, Tumor
Genetics
Cancer Genetics
Humans
Gene
Gene Expression Profiling
Data Visualization
Carcinoma
Cancers and Neoplasms
Biology and Life Sciences
Hepatocellular Carcinoma
Oncogenes
Expression (mathematics)
Genitourinary Tract Tumors
Gene Ontology
Cancer biomarkers
Biomarkers
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....77f2809f93c15a8ea4dbe9c410d59884