Faculty Profiles

Sun, S., Pritchard, A., McFall, E., & Tian, C. (2025). Statistical analysis of DNA methylation patterns of tumor suppressor genes in breast cancer. Epigenetics Insights, 18(1). Retrieved from https://www.maxapress.com/article/id/67fc62abfa6c5866a7a9383b

Sun, S., & Zamora, R. (2024, December). Mcperturb (software package): https://github.com/ss355/mcperturb.

Sun, S., & Zamora, R. (2024). Mcperturb: A New 5‑step Multicollinearity Diagnostic Method Developed Based on Existing Methods. Statistics in Biosciences.

Sun, S., Cheng, F., Han, D., Wei, S., Zhong, A., Massoudian, S., & Johnson, A. B. (2023). Pairwise comparative analysis of six haplotype assembly methods based on users’ experience. BMC Genomic Data, 24(35).

Sun, S., Dammann, J., Lai, P., & Tian, C. (2022). Thorough statistical analyses of breast cancer co-methylation patterns. BMC Genomics Data, 23(1), Page 1-23.

Sun, S., Zane, A., Fulton, C., & Philipoom, J. (2021). Statistical and bioinformatic analysis of hemimethylation patterns in non-small cell lung cancer. BMC Cancer, 21. https://doi.org/10.1186/s12885-021-07990-7

Sun, L., Namboodiri, S., Chen, E., & Sun, S. (2019). Preliminary Analysis of Within-sample Co-methylation Patterns in Normal and Cancerous Breast Samples. Cancer Informatics, 18, 1–14.

Sun, S., Lee, Y. R., & Enfield, B. (2019). Hemimethylation Patterns in Breast Cancer Cell Lines. Cancer Informatics, 18, 1–12.

Sun, L., & Sun, S. (2019). Within-sample co-methylation patterns in normal tissues. Biodata Mining, 12(9).

Li, C., Lee, J., Ding, J., & Sun, S. (2018). Integrative analysis of gene expression and methylation data for breast cancer cell lines. BioData Mining, 11(13).

Elston, E., Satagopan, J., & Sun, S. (2017). Statistical Genetic Terminology. In Statistical Human Genetics: Methods and Protocols (Methods in Molecular Biology). United States: Humana Press.

Sun, S., & Yu, X. (2016). HMM-Fisher: Identifying differential methylation using a hidden Markov model and Fisher’s exact test. Statistical Application in Genetics and Molecular Biology 2016, 15, 55–67.

Yu, X., & Sun, S. (2016). HMM-DM: Identifying differentially methylated regions using a hidden Markov model. Statistical Application in Genetics and Molecular Biology 2016, 15(1), 69–81.

Yu, X., & Sun, S. (2016). Comparing five statistical methods of differential methylation identification using bisulfite sequencing data. Statistical Application in Genetics and Molecular Biology 2016, 15(2), 173–191.

Sun, S., & Yu, X. (2016). HMM-Fisher: Software package for differential methylation identification. Retrieved from https://github.com/xxy39/HMM-Fisher

Yu, X., & Sun, S. (2016). HMM-DM: Software package for differential methylation identification. Retrieved from https://github.com/xxy39/HMM-DM

Tian, S., Bertelsmann, K., Yu, L., & Sun, S. (2016). DNA methylation heterogeneity patterns in breast cancer cell lines. Cancer Informatics, 2016, 15 (Suppl 4), 1–9. https://doi.org/10.4137/CIN.S40300

Kenyon, J., Nickel-Meester, G., Qing, Y., Santos-Guasch, G., Drake, E., Fu, P., … Gerson, S. (2016). Epigenetic Loss of MLH1 Expression in Normal Human Hematopoietic Stem Cell Clones is Defined by the Promoter CpG Methylation Pattern Observed by High-Throughput Methylation Specific Sequencing. International Journal of Stem Cell Research & Therapy, 3(2), 1–24.

Sun, S., & Li, P. (2015). HMPL: A pipeline for identifying hemimethylation patterns by comparing two samples. Cancer Informatics 2015, 14(Suppl 2), 235–245.

Xu, L., Mitra-Behura, S., Alston, B., Zong, Z., & Sun, S. (2015). Identifying DNA methylation variation patterns to obtain potential breast cancer biomarker genes. Journal of Biomedical Data Mining 2015, 4, 115.

Sun, S., & Li, P. (2015). HMPL: A pipeline for identifying hemimethylation patterns by comparing two samples. Retrieved from http://hal.case.edu/~sun/HMPL/HMPL.zip

Sun, S., Noviski, A., & Yu, X. (2013). MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment. BMC Bioinformatics 2013, 14(259), 1–9.

Yu, X., & Sun, S. (2013). Comparing a few SNP calling algorithms using low-coverage sequencing data. BMC Bioinformatics 2013, 14(274), 1–15.

Sun, S., Noviski, A., & Yu, X. (2013). MethyQA: A pipeline for methylation sequencing quality assessment (using perl & R). Retrieved from http://hal.case.edu/~sun/MethyQA.v2.zip

Elston, E., Satagopan, J., & Sun, S. (Eds.). (2012). Statistical Human Genetics: Methods and Protocols (Methods in Molecular Biology). United States: Humana Press.

Adams, M., Veigl, M., Wang, Z., Molyneux, N., Sun, S., Guda, K., … Willis, J. (2012). Global Mutational Profiling of Formalin Fixed Human Colon Cancers from a Pathology Archive. Modern Pathology 2012, 25, 1599–1600.

Yu, X., Guda, K., Willis, J., Veigl, M., Wang, Z., Markowitz, S., … Sun, S. (2012). How well do alignment programs perform on sequencing data with varying qualities and from repetitive regions? BioData Mining 2012, 5(6).

Sun, S., Chen, Z., Yan, P., Huang, Y., Huang, T., & Lin, S. (2011). Identifying hypermethylated CpG islands using a quantile regression model. BMC Bioinformatics 2011, 12(54).

Sun, S., Huang, Y., Yan, P., Huang, T., & Lin, S. (2011). Preprocessing differential methylation hybridization microarray data. BioData Mining 2011, 4(13).

Han, L., Zheng, S., Sun, S., Huang, T., & Zhao, Z. (2010). Genome-wide DNA methylation profiling in 40 breast cancer cell lines. Advanced Intelligent Computing Theories and Applications. Lecture Notes in Computer Science, 6215, 277–284.

Greenwood, C., Sun, S., Veenstra, J., Hamel, N., Niell, B., Gruber, S., & Foulkes, W. (2010). How old is this mutation? – a study of three Ashkenazi Jewish founder mutations. BMC Genetics 2010, 11(39).

Sun, S., Yan, P., Huang, T., & Lin, S. (2009). Identifying differentially methylated genes using mixed effect and generalized least square models. BMC Bioinformatics 2009, 10, 404.

Clendenning, M., Baze, M. E., Sun, S., Walsh, K., Liyanarachchi, S., Fix, D., … de la Chapelle, A. (2008). Origins and Prevalence of the American Founder Mutation of MSH2. Cancer Research 2008, 68(7), 2145–2153.

Clendenning, M., Senter, L., Hampel, H., Robinson, K. L., Sun, S., Buchanan, D., … de la Chapelle, A. (2008). A frame-shift mutation of PMS2 is a widespread cause of Lynch syndrome. Journal of Medical Genetics 2008, 45, 340–345.

Lin, H. L., Zuo, T., Lin, C., Kuo, C. T., Liyanarachchi, S., Sun, S., … Huang, T. H. (2008). Breast cancer-associated fibroblasts confer AKT1-mediated epigenetic silencing of cystatin M in epithelial cells. Cancer Research 2008, 68, 10257–10266.

Sun, S., Potter, D., Yan, P., Huang, T., & Lin, S. (2008). A quantile approach to analyzing differential methylation hybridization microarrays (pp. 87–90).

Sun, S., Greenwood, C. M., & Neal, R. M. (2007). Haplotype inference using a Bayesian Hidden Markov Model. Genetic Epidemiology 2007, 31(8), 937–948.

Sun, S. (2007). HMM.hap: R program for haplotype inferences.

Rahman, P., Sun, S., Peddle, L., Snelgrove, T., Greenwood, C. M., & Gladman, D. (2006). Association between the interleukin-1 family gene cluster and psoriatic arthritis. Arthritis & Rheumatism 2006, 54(7), 2321–2325.

Sun, S., Greenwood, C. M., Thiffault, I., Hamel, N., Chong, G., & Foulkes, W. D. (2005). The HNPCC associated MSH2*1906G->C founder mutation probably originated between 1440 CE and 1715 CE in the Ashkenazi Jewish population. Journal of Medical Genetics 2005, 42, 766–768.

Butt, C., Sun, S., Peddle, L., Greenwood, C. M., Hamilton, S., Gladman, D., & Rahman, P. (2005). Association of Nuclear Factor-kB in Psoriatic Arthritis. Journal of Rheumatology 2005, 32, 1742–1744.

Butt, C., Sun, S., Greenwood, C. M., Gladman, D., & Rahman, P. (2005). Lack of association of SLC22A4, SLC22A5, SLC9A3R1 and RUNX1 variants in psoriatic arthritis. Rheumatology 2005, 44, 820–821.

Sun, S., & Liu, Z. (2000). Cohomology Complex Projective Space with Actions of G5. Northeastern Mathematical Journal 2000, 16(3), 307–314.

Sun, S., Zhao, Q., & Liu, J. (1995). Managing Program for Operating Overhead Traveling Cranes.