Wu Leihong, Liu Zhichao, Xu Joshua, Chen Minjun, Fang Hong, Tong Weida, Xiao Wenming
Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
Office of Scientific Coordination, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
Biomark Med. 2015;9(11):1053-65. doi: 10.2217/bmm.15.96. Epub 2015 Oct 26.
To evaluate gene signature and network-based approach for cancer subtyping and classification.
MATERIALS & METHODS: Here we introduced NETwork Based clustering Approach with Gene signatures (NETBAGs) algorithm, which clustered samples based on gene signatures and identified molecular markers based on their significantly expressed gene network profiles.
Applying NETBAGs to multiple independent breast cancer datasets, we demonstrated that the clustering results were highly associated with the clinical subtypes and clearly revealed the genomic diversity of breast cancer samples.
NETBAGs algorithm is able to classify samples by their genomic signatures into clinically significant phenotypes so that potential biomarkers can be identified. The approach may contribute to cancer research and clinical study of complex diseases.
评估基于基因特征和网络的方法用于癌症亚型分类。
在此我们引入了基于网络的基因特征聚类方法（NETBAGs）算法，该算法基于基因特征对样本进行聚类，并根据显著表达的基因网络图谱识别分子标记。
将NETBAGs应用于多个独立的乳腺癌数据集，我们证明聚类结果与临床亚型高度相关，并清楚地揭示了乳腺癌样本的基因组多样性。
NETBAGs算法能够根据基因组特征将样本分类为具有临床意义的表型，从而识别潜在的生物标志物。该方法可能有助于癌症研究和复杂疾病的临床研究。
