ArticleNucleic acids research2013
ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 46 citations in OpenAlex.
- PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein-Protein Interactions, Co-Expression Networks, and Expression Data.International journal of molecular sciences · 2024Article
- Iterative sure independent ranking and screening for drug response prediction.BMC medical informatics and decision making · 2020Article
- Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine.Advanced materials (Deerfield Beach, Fla.) · 2020Review
- Identification of potential genes and pathways for response prediction of neoadjuvant chemoradiotherapy in patients with rectal cancer by systemic biological analysis.Oncology letters · 2019Article
- Screening candidate microRNA-mRNA regulatory pairs for predicting the response to chemoradiotherapy in rectal cancer by a bioinformatics approach.Scientific reports · 2017Article
- An Efficient Approach to Screening Epigenome-Wide Data.BioMed research international · 2016Article
- Identifying network biomarkers based on protein-protein interactions and expression data.BMC medical genomics · 2015Article
- Machine learning applications in cancer prognosis and prediction.Computational and structural biotechnology journal · 2015Review
- Article
- Pathway and network analysis in proteomics.Journal of theoretical biology · 2014Review
- Big biological data: challenges and opportunities.Genomics, proteomics & bioinformatics · 2014Article
- iPcc: a novel feature extraction method for accurate disease class discovery and prediction.Nucleic acids research · 2013Article
- Promote connections of young computational biologists in China.Genomics, proteomics & bioinformatics · 2013Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN.
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