Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge

Arshdeep Sekhon, Zhe Wang, Yanjun Qi
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:10881-10923, 2022.

Abstract

Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential network estimator that allows integrating various sources of knowledge beyond data samples. The proposed estimator is scalable to a large number of variables and achieves a sharp asymptotic convergence rate. Empirical experiments on extensive simulated data and four real-world applications (one on neuroimaging and three from functional genomics) show that our approach achieves improved differential network estimation and provides better supports to downstream tasks like classification. Our results highlight significant benefits of integrating group, spatial and anatomic knowledge during differential genetic network identification and brain connectome change discovery.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-sekhon22a, title = { Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge }, author = {Sekhon, Arshdeep and Wang, Zhe and Qi, Yanjun}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {10881--10923}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/sekhon22a/sekhon22a.pdf}, url = {https://proceedings.mlr.press/v151/sekhon22a.html}, abstract = { Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential network estimator that allows integrating various sources of knowledge beyond data samples. The proposed estimator is scalable to a large number of variables and achieves a sharp asymptotic convergence rate. Empirical experiments on extensive simulated data and four real-world applications (one on neuroimaging and three from functional genomics) show that our approach achieves improved differential network estimation and provides better supports to downstream tasks like classification. Our results highlight significant benefits of integrating group, spatial and anatomic knowledge during differential genetic network identification and brain connectome change discovery. } }
Endnote
%0 Conference Paper %T Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge %A Arshdeep Sekhon %A Zhe Wang %A Yanjun Qi %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-sekhon22a %I PMLR %P 10881--10923 %U https://proceedings.mlr.press/v151/sekhon22a.html %V 151 %X Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential network estimator that allows integrating various sources of knowledge beyond data samples. The proposed estimator is scalable to a large number of variables and achieves a sharp asymptotic convergence rate. Empirical experiments on extensive simulated data and four real-world applications (one on neuroimaging and three from functional genomics) show that our approach achieves improved differential network estimation and provides better supports to downstream tasks like classification. Our results highlight significant benefits of integrating group, spatial and anatomic knowledge during differential genetic network identification and brain connectome change discovery.
APA
Sekhon, A., Wang, Z. & Qi, Y.. (2022). Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:10881-10923 Available from https://proceedings.mlr.press/v151/sekhon22a.html.

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