Sanjeena’s Google Scholar author profile.

Publications: Appeared and Accepted

  • Silva, A., Rothstein, S. J., McNicholas, P. D. Subedi, S. (2019) A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data. BMC Bioinformatics. 20(1) 394.
  • Dang, S. and Vialaneix, N. (2018) Cutting Edge Bioinformatics and Biostatistics Approaches Are Bringing Precision Medicine and Nutrition to a New Era. Lifestyle Genomics 1–4.
  • Flaherty, E. J., Lum, G. B., DeEII, J. R., Subedi, S., Shelp, B. J., Bozzo, G. G. (2018) Metabolic Alterations in Postharvest Pear Fruit As Influenced by 1-Methylcyclopropene and Controlled Atmosphere Storage. Journal of Agricultural Food and Chemistry, 66(49) 12989-12999.
  • Brikis, C.J., Zarei, A., Chiu,G.Z., Deyman, K. L., Liu, J., Trobacher, C. P., Hoover, G.J., Subedi, S., DeEll, J. R., Bozzo, G. G., Shelp, B.J. (2018) Targeted quantitative profiling of metabolites and gene transcripts associated with 4-aminobutyrate (GABA) in apple fruit stored under multiple abiotic stresses. Horticulture Research, 5(1) 61.
  • Liu, J., Abdelmagid, S. A., Pinelli, C. J., Monk, J.M., Liddle, D.M., Hillyer, L. M., Hucik, B., Silva, A., Subedi, S., Wood, G. A., Robinson, L. E., Muller, W. J., and Ma, D. W.L. Marine fish oil is more potent than plant based n-3 polyunsaturated fatty acids in the prevention of mammary tumours. The Journal of Nutritional Biochemistry. 55 41–52
  • Carbonara, J., Dang, S., Gelsomini, F., Kanev, K., Sperhac, J., Walters, L. (2017) A Multifaceted Approach Towards Education in Data Analytics. Recent Advances in Technology Research and Education: Proceedings of the 16th International Conference on Global Research and Education Inter-Academia 2017 660 307.
  • Coutin, J. A. F., Munholland, S., Silva, A., Subedi, S., Lukens, L., Crosby, W. L., … & Bozzo, G. G. (2017). ‘Proanthocyanidin accumulation and transcriptional responses in the seed coat of cranberry beans (Phaseolus vulgaris L.) with different susceptibility to postharvest darkening’. BMC Plant Biology, 17(1), 89.
  • Lum, G. B., DeEll, J. R., Hoover, G. J., Subedi, S., Shelp, B. J., & Bozzo, G. G. (2017). ‘1-Methylcylopropene and controlled atmosphere modulate oxidative stress metabolism and reduce senescence-related disorders in stored pear fruit’. Postharvest Biology and Technology, 129, 52-63.
  • Roke, K., Walton, K., Klingel, S. L., Harnett, A., Subedi, S., Haines, J., & Mutch, D. M. (2017). `Evaluating Changes in Omega-3 Fatty Acid Intake after Receiving Personal FADS1 Genetic Information: A Randomized Nutrigenetic Intervention’. Nutrients, 9(3), 240.
  • Lum, G. B., Brikis, C. J., Deyman, K. L., Subedi, S., DeEll, J. R., Shelp, B. J., & Bozzo, G. G. (2016). ‘Pre-storage conditioning ameliorates the negative impact of 1-methylcyclopropene on physiological injury and modifies the response of antioxidants and γ-aminobutyrate in ‘Honeycrisp’apples exposed to controlled-atmosphere conditions’. Postharvest Biology and Technology, 116, 115-128.
  • Sung, Y., Feng, Z., Subedi, S. (2016), ‘A genome-wide association study of multiple longitudinal traits with related subjects’, Stat. 5(1),22-44 [doi].
  • Lum, G. B., Brikis, C. J., Deyman, K. L., Subedi, S., DeEII, J. R., Shelp, B. J., Bozzo, G. B. (2016), ‘Pre-storage conditioning ameliorates the negative impact of 1-methylcyclopropene on physiological injury and alters the response of antioxidants and γ-aminobutyrate in `Honeycrisp’ apples exposed to controlled-atmosphere conditions’, Postharvest Biology and Technology. 116,115-128 [doi].
  • McNicholas, P.D. and Subedi, S. (2016), ‘Discussion of “Perils and potentials of self-selected entry to epidemiological studies and surveys”‘, Journal of the Royal Statisical Society: Series A. 179(2), 362-363 [doi].
  • Subedi, S., Punzo, A., Ingrassia, S. and McNicholas, P.D. (2015), ‘Cluster-Weighted t-Factor Analyzers for Robust Model-based Clustering and Dimension Reduction’. Statistical Methods and Applications.24(4), 623-649. [doi].
  • Subedi, S. and McNicholas, P. D. (2015), ‘Discussion of “Analysis of forensic DNA mixtures with artefacts'”, Journal of the Royal Statistical Society: Series C 64(1), 43-44. [doi].
  • Misyura, M., Guevara, D., Subedi, S., Hudson, D., McNicholas, P. D., Colasanti, J, and Rothstein, S. (2014), ‘Nitrogen limitation and high density stress responses in rice suggest a role for ethylene in intraspecific competition’, BMC Genomics 15(1), 681. [doi]
  • Subedi, S. and McNicholas, P. D. (2014), ‘Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions’, Advances in Data Analysis and Classification 8(2), 167-193. [doi]
  • Makhmudova, A., Williams, D., Brewer, D., Massey, S., Patterson, J., Silva, A, Vassall, K., Liu, F., Subedi, S., Harauz, G., Siu, K. W. M., Tetlow, I. J. and Emes, M. J. (2014), ‘Identication of Multiple Phosphorylation Sites on Maize Endosperm Starch Branching Enzyme IIb, a Key Enzyme in Amylopectin Biosynthesis’, Journal of Biological Chemistry 289(13), 9233-9246. [doi]
  • Subedi, S., Punzo, A., Ingrassia, S. and McNicholas, P. D. (2013), `Clustering and classification via cluster-weighted factor analyzers’, Advances in Data Analysis and Classification 7(1), 5-40. [doi]
  • Humbert, S., Subedi, S., Zeng, B., Bi, Y., Chen,X., Zhu, T., McNicholas, P. D., Rothstein, S. J. (2013), `Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses’, BMC Genomics 14(3). [doi]
  • Subedi, S., Feng, Z. Z., Deardon, R. and Schenkel, F. (2013), `SNP selection for predicting a quantitative trait’, Journal of Applied Statistics 40(3), 600-613. [doi]
  • McNicholas, P. D. and Subedi, S. (2012), `Clustering gene expression time course data using mixtures of multivariate t-distributions’, Journal of Statistical Planning and Inference 142(5), 1114-1127. [doi]
  • Feng, Z, Yang, X., Subedi, S. and McNicholas, P. D. (2012), `The LASSO and sparse least square regression methods for SNP selection in predicting quantitative traits’, IEEE Transactions on Computational Biology and Bioinformatics 9(2), 629-63. [doi]
  • Andrews, J. L., McNicholas, P. D. and Subedi, S. (2011), `Model-based classification via mixtures of multivariate t-distributions’, Computational Statistics and Data Analysis 55(1), 520-529. [doi]

Software Development

  • McNicholas, P.D., Jampani, K. R. and Subedi, S. (2015), longclust: Model-based clustering and classication for longitudinal data (R package version 1.2).
  • Harvey, D. S., Subedi, S., Hanner, R., Adamowicz, S. (2015), ePRIMER: primer design software for environmental DNA studies.

Refereed Journal Articles: Submitted / In preparation

  • Silva, A., Rothstein, S. J., McNicholas, P. D., and Subedi, S. Finite Mixtures of Matrix Variate Poisson- Log Normal Distributions for Three-Way Count Data. Submitted.
  • Subedi, S. and McNicholas, P. D., A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting. Submitted.
  • Neish, D., Subedi, S., Feng, Z. Cluster Analysis Of Microbiome Data Via Mixtures Of Dirichlet- Multinomial Regression Models. Submitted.
  • Fang, Y., Karlis, D., Subedi, S., A Bayesian Approach for Clustering Skewed Data Using Mixtures of Multivariate Normal-Inverse Gaussian Distributions. Submitted.

Non-Refereed Contributions

  • Subedi, S. (2012), `Variational Approximations and Other Topics in Mixture Models’, Ph.D. thesis, University of Guelph.
  • Subedi, S. (2009), `Genome Selection for Predicting the Estimated Breeding Value of Canadian Holstein Cattle’, Master’s thesis, University of Guelph.
  • Andrews, J. L., Diaz Bobadilla, I. E., Huang, Y., Kitchen, C., Malenfant, K., Moloney, P. D., Steane, M. A., Subedi, S., Xu, R., Zhang, X., McNicholas, P. D. and Stockie, J. M. (2009), `Early detection of important animal health events’ in Proceedings of the PIMS-MITACS Industrial Problem Solving Workshop, Calgary, Alberta.
  • Andrews, J. L., Haroutunian J., Steane, M. A., Subedi, S., Zhang, X. and McNicholas, P. D. (2009), `Automatic classification and variable reduction for food authenticity problems’ in Proceedings of the PIMS-MITACS Graduate Industrial Mathematical Modelling Camp, Calgary, Alberta.
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