SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes

Junetae Kim, Kyoungsuk Park, Hanseok Jeong, Youngwook Kim, Jeongseon Kim, Sun-Young Kim
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:16771-16786, 2023.

Abstract

Identifying prognostic genes associated with patient survival is an important goal in cancer genomics, as this information could inform treatment approaches and improve patient outcomes. However, the identification of prognostic genes is complicated by the high dimensionality of genetic data, which makes their identification computationally intensive. Furthermore, most cancer genomics studies lack appropriate low-risk groups against which to compare. To address these issues, we present a framework that identifies candidate prognostic genes by integrating representation learning and statistical analysis approaches. Specifically, we propose a collaborative filtering-derived mechanism to represent patients in order of their survival risk, facilitating their dichotomization. We also propose a mechanism that allows embedded gene vectors to be polarized on the extremities of, or centered on, both reference axes to facilitate recommendations. Restricting our analysis to a few representative genes within each cluster allowed for the efficient identification of prognostic genes. Finally, we demonstrate the potential of this proposed framework for identifying prognostic genes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-kim23s, title = {{S}ur{P}ro{G}enes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes}, author = {Kim, Junetae and Park, Kyoungsuk and Jeong, Hanseok and Kim, Youngwook and Kim, Jeongseon and Kim, Sun-Young}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {16771--16786}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/kim23s/kim23s.pdf}, url = {https://proceedings.mlr.press/v202/kim23s.html}, abstract = {Identifying prognostic genes associated with patient survival is an important goal in cancer genomics, as this information could inform treatment approaches and improve patient outcomes. However, the identification of prognostic genes is complicated by the high dimensionality of genetic data, which makes their identification computationally intensive. Furthermore, most cancer genomics studies lack appropriate low-risk groups against which to compare. To address these issues, we present a framework that identifies candidate prognostic genes by integrating representation learning and statistical analysis approaches. Specifically, we propose a collaborative filtering-derived mechanism to represent patients in order of their survival risk, facilitating their dichotomization. We also propose a mechanism that allows embedded gene vectors to be polarized on the extremities of, or centered on, both reference axes to facilitate recommendations. Restricting our analysis to a few representative genes within each cluster allowed for the efficient identification of prognostic genes. Finally, we demonstrate the potential of this proposed framework for identifying prognostic genes.} }
Endnote
%0 Conference Paper %T SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes %A Junetae Kim %A Kyoungsuk Park %A Hanseok Jeong %A Youngwook Kim %A Jeongseon Kim %A Sun-Young Kim %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-kim23s %I PMLR %P 16771--16786 %U https://proceedings.mlr.press/v202/kim23s.html %V 202 %X Identifying prognostic genes associated with patient survival is an important goal in cancer genomics, as this information could inform treatment approaches and improve patient outcomes. However, the identification of prognostic genes is complicated by the high dimensionality of genetic data, which makes their identification computationally intensive. Furthermore, most cancer genomics studies lack appropriate low-risk groups against which to compare. To address these issues, we present a framework that identifies candidate prognostic genes by integrating representation learning and statistical analysis approaches. Specifically, we propose a collaborative filtering-derived mechanism to represent patients in order of their survival risk, facilitating their dichotomization. We also propose a mechanism that allows embedded gene vectors to be polarized on the extremities of, or centered on, both reference axes to facilitate recommendations. Restricting our analysis to a few representative genes within each cluster allowed for the efficient identification of prognostic genes. Finally, we demonstrate the potential of this proposed framework for identifying prognostic genes.
APA
Kim, J., Park, K., Jeong, H., Kim, Y., Kim, J. & Kim, S.. (2023). SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:16771-16786 Available from https://proceedings.mlr.press/v202/kim23s.html.

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