Mojtaba Kanani Sarcheshmeh, MSc student – University of Calgary

Supervisor – Qingrun Zhang

Collaborator – Dr. Xingyi Guo, and his group at Vanderbilt University Medical Center

Project Description: This project aims to develop a novel mathematical model and its scalable
implementation for high-dimensional feature selection with prior domain
knowledge integrated. In particular, we will develop hierarchical penalized
regression techniques by extending Group Lasso [1], LassoNet [2], and Graphical
Lasso [3]. The application will be to identify key genomic signatures and gene
networks associated with cancer prognosis and treatment response. Group Lasso
selects variable groups, identifying pathways or co-regulated gene sets linked to
clinical outcomes. Graphical Lasso estimates a sparse inverse covariance matrix,
uncovering co-expression networks and gene interactions. LassoNet, a lasso
penalized neural network, efficiently selects predictive multi-gene signatures from
high-dimensional data. By integrating these methods, we aim to gain a
comprehensive view of interacting terms which are critical in the context of
cancer progression and treatment. Based upon our existing publications [4–7], we
will focus on theoretical and algorithmic innovations to extend these methods to
high-dimensional data settings common in cancer research. In collaboration with
other professors in Math/Stats and Cumming School of Medicine at the
University of Calgary, we will also apply the methods to the large-scale cancer
omics data that we have access through the TCGA and CPTAC Consortium,
the largest repository of cancer data generation in the world [8–33].

References
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[2] Ismael Lemhadri et al. “Lassonet: A neural network with feature sparsity”. In: Journal of Machine Learning
Research 22.127 (2021), pp. 1–29.
[3] Jerome Friedman, Trevor Hastie, and Robert Tibshirani. “Sparse inverse covariance estimation with the
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[5] Jingni He, Qing Li, and Qingrun Zhang. “rvTWAS: identifying gene–trait association using sequences by
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[14] Hui Zhang et al. “Integrated proteogenomic characterization of human high-grade serous ovarian cancer”. In:
Cell 166.3 (2016), pp. 755–765.
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