ArticleScientific reports2013
Identification of breast cancer patients based on human signaling network motifs.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrating protein interaction and pathway crosstalk network reveals a promising therapeutic approach for psoriasis through apoptosis induction.Scientific reports · 2024Article
- Review of tools and algorithms for network motif discovery in biological networks.IET systems biology · 2020Review
- Disjoint motif discovery in biological network using pattern join method.IET systems biology · 2019Article
- Network motifs and their origins.PLoS computational biology · 2019Review
- Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.Nucleic acids research · 2018Article
- Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic FrameworkAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Network motif-based method for identifying coronary artery disease.Experimental and therapeutic medicine · 2016Article
- Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks.BMC systems biology · 2015Article
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Authors and funding
11 authors.
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Abstract
Identifying breast cancer patients is crucial to the clinical diagnosis and therapy for this disease. Conventional gene-based methods for breast cancer diagnosis ignore gene-gene interactions and thus may lead to loss of power. In this study, we proposed a novel method to select classification features, called "Selection of Significant Expression-Correlation Differential Motifs" (SSECDM). This method applied a network motif-based approach, combining a human signaling network and high-throughput gene expression data to distinguish breast cancer samples from normal samples. Our method has higher classification performance and better classification accuracy stability than the mutual information (MI) method or the individual gene sets method. It may become a useful tool for identifying and treating patients with breast cancer and other cancers, thus contributing to clinical diagnosis and therapy for these diseases.
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