Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
This work addresses the challenge of differential network estimation for applications such as neuroimaging and functional genomics, offering incremental improvements by incorporating external knowledge.
The paper tackles the problem of estimating differential statistical dependency networks in high-dimensional, low-sample settings by proposing a novel estimator that integrates various sources of knowledge beyond data samples. The result is a scalable method with sharp asymptotic convergence rates, showing improved estimation and better support for downstream tasks like classification in simulated and real-world applications.
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.