Alan L. Chang

TL;DR: This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth.

TL;DR: This work introduces a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge.

TL;DR: VoPo (VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets, outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

TL;DR: In this paper, the authors review advanced machine learning methods that could provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

TL;DR: In this article , a system biology approach incorporating ante-and post-natal risk factors and analyzing omic and multi-omic data using machine learning is proposed for further elucidating biologic mechanisms of fetal and neonatal brain injury.