ArticleOncology letters2018
A curated target gene pool assisting disease prediction and patient-specific biomarker selection for lung squamous cell carcinoma.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes.Frontiers in bioengineering and biotechnology · 2019Article
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Authors and funding
4 authors.
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Abstract
There have been hundreds of genes demonstrated to be associated with lung squamous cell carcinoma (LSCC), presenting various degrees of association with this disease. In the present study, gene vectors were investigated as genetic biomarkers for the diagnosis and personalized treatment of LSCC. A LSCC genetic database (LSCC_GD) was developed through literature-associated data analysis, where 260 LSCC target genes were curated. Subsequently, numerous associations between these genes and LSCC were studied. Following this, a sparse representation-based variable selection (SRVS) was employed for gene selection from two LSCC gene expression datasets, followed by a case/control classification. Results were compared using analysis of variance (ANOVA)-based gene selection approaches. Using SRVS, a gene vector was selected from each dataset, resulting in significantly higher classification accuracy (CR), compared with randomly selected genes (For datasets GSE18842 and GSE1987, CR=100 and 100% and permutation P=5.0×10
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