Publications

Sanjeena’s Google Scholar author profile.

Publications: Appeared and Accepted

• Silva, A., Qin, X., Rothstein, S. J., McNicholas, P. D., and Subedi, S. `Finite Mixtures of Matrix Variate Poisson- Log Normal Distributions for Three-Way Count Data'. Bioinformatics. To Appear.

• Ciobani, C., Mcnairn, C., Nyiri, B., Chauhan, V., Subedi, S., and Murugkar, S. `Exploring the use of Raman spectroscopy and covariate-adjusted multivariate analysis for the detection of irradiated blood'. Radiation Research. To Appear.

• Tu, W. and Subedi, S. Penalized logistic normal multinomial factor analyzers for high dimensional compositional data. Journal of Statistical Research. To Appear.

• Beausoleil-Morrison, A., McNairn, C., Qin, X. Ciobanu, C., Altwasser, K., Subedi, S., Chauhan, V., Murugkar, S. (2023) `Application of Raman spectroscopy and multivariate analysis to detect ionizing radiation-induced changes in blood plasma'. Proceedings SPIE 12373, Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 123730A.

• Fang, Y., Karlis, D., and Subedi, S. (2022) `An infinite mixtures of multivariate normal-inverse Gaussian distributions for clustering of skewed data'. Journal of Classification. 39, 510-552.[doi] 

• Yu, J., Tu, W., Payne, A., Rudyk, C., Cuadros Sanchez, S., Khilji, S., Kumarathasan, P., Subedi, S., Haley, B., Wong, A. and Anghel, C., (2022) `Adverse Outcome Pathways and Linkages to Transcriptomic Effects Relevant to Ionizing Radiation Injury’. International Journal of Radiation Biology. 98(12) 1789-1801 [doi].

• Tu, W. and Subedi, S., (2022) `A family of mixture models for biclustering'. Statistical Analysis and Data mining. 15(2) 206-224 [doi] [R-Code] [Preprint].

• Subedi, S. and McNicholas, P. D. (2021), `A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting. Journal of Classification. 38(1) 89–108 [doi].

• Subedi, S. and Browne, R. P. (2020) A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data. Stat. 9(1) e310 [doi].

• Subedi, S., Neish, D., Bak, S., and Feng, Z. (2020)`Cluster analysis of microbiome data via mixtures of Dirichlet-multinomial regression models’. Journal of Royal Statistical Society: Series C. 69(5) 1163-1187 [doi].

• Malik, M., Subedi, S., Marques, C.N.H, and Mahler, G. (2020)`Bacteria remediate the effects of food additives on intestinal function in an in vitro model of the gastrointestinal tract'. Frontiers in Nutrition. 7 131[doi].

• 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 [doi].

• 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 [doi].

• 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 [doi].

• 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 [doi].

• 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. (2018) 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 [doi].

• 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 [doi].

• 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 [doi].

• 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 [doi].

• 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 [doi].

• 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 [doi].

• 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]

Refereed Journal Articles: Submitted / In preparation

• Livochkaa, A., Browne, R., and Subedi, S. Estimation of Gaussian bi-clusters with general block-diagonal covariance matrix and applications. Submitted

• Fang, Y., Karlis, D., Subedi, S., A Bayesian Approach for Clustering Skewed Data Using Mixtures of Multivariate Normal-Inverse Gaussian Distributions. Submitted.

• Subedi, S. Clustering matrix variate longitudinal count data. Submitted.

• Tu, W. and Subedi, S. Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data. Submitted.

• Fang, Y., Karlis, D., and Subedi, S. A Bayesian Approach for Clustering Skewed Data Using Mixtures of Multivariate Normal-Inverse Gaussian Distributions. In preparation.

• Fang, Y. and Subedi, S. Variational inference of logistic-normal multinomial mixture model for clustering microbiome data. In preparation.

• Tu, W., Fang, Y., and Subedi, S. Logistic Normal Multinomial Biclustering Mixture Model for Microbiome Count Data. In preparation.

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.