Bull Math Biol https://doi.org/10.1007/s11538-018-0449-8 ORIGINAL ARTICLE Sparse Representation-Based Patient-Speciﬁc Diagnosis and Treatment for Esophageal Squamous Cell Carcinoma 1 2 3 Bin Huang · Ning Zhong · Lili Xia · 1 4,5 Guiping Yu · Hongbao Cao Received: 11 July 2017 / Accepted: 25 May 2018 © Society for Mathematical Biology 2018 Abstract Precision medicine and personalized treatment have attracted attention in recent years. However, most genetic medicines mainly target one genetic site, while complex diseases like esophageal squamous cell carcinoma (ESCC) usually present heterogeneity that involves variations of many genetic markers. Here, we seek an approach to leverage genetic data and ESCC knowledge data to forward personal- ized diagnosis and treatment for ESCC. First, 851 ESCC-related gene markers and their druggability were studied through a comprehensive literature analysis. Then, a sparse representation-based variable selection (SRVS) was employed for patient- speciﬁc genetic marker selection using gene expression datasets. Results showed that the SRVS method could identify a unique gene vector for each patient group, leading to signiﬁcantly higher classiﬁcation accuracies compared to randomly selected genes (100, 97.17, 100, 100%; permutation p values: 0.0032, 0.0008, 0.0004, and 0.0008). The SRVS also outperformed an ANOVA-based gene selection method in terms of Bin
Bulletin of Mathematical Biology – Springer Journals
Published: Jun 4, 2018
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