Simple and scalable algorithms for cluster-aware precision medicine

Amanda M. Buch, Conor Liston, Logan Grosenick
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:136-144, 2024.

Abstract

AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.

Cite this Paper


BibTeX
@InProceedings{pmlr-v238-buch24a, title = {Simple and scalable algorithms for cluster-aware precision medicine}, author = {Buch, Amanda M. and Liston, Conor and Grosenick, Logan}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {136--144}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/buch24a/buch24a.pdf}, url = {https://proceedings.mlr.press/v238/buch24a.html}, abstract = {AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.} }
Endnote
%0 Conference Paper %T Simple and scalable algorithms for cluster-aware precision medicine %A Amanda M. Buch %A Conor Liston %A Logan Grosenick %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-buch24a %I PMLR %P 136--144 %U https://proceedings.mlr.press/v238/buch24a.html %V 238 %X AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.
APA
Buch, A.M., Liston, C. & Grosenick, L.. (2024). Simple and scalable algorithms for cluster-aware precision medicine. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:136-144 Available from https://proceedings.mlr.press/v238/buch24a.html.

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