Ziwei Huang

RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the limited-sample-size challenges common in high-dimensional (e.g., ‘omics’) data. It supports both the estimation of partial correlation networks modeled as Gaussian networks¹ and conditional Gaussian Bayesian networks for mixed data types that combine continuous and discrete variables². The framework incorporates multiple resampling strategies, including bootstrap, subsampling, and cluster-based approaches, to accommodate both independent and correlated (e.g., family-based) observations. To enhance interpretability, RSNet integrates graphlet-based topology analysis that captures higher-order connectivity and edge sign information, enabling single-node and subnetwork-level insights. Notably, RSNet is the first R package to efficiently construct signed graphlet degree vector matrices (GDVMs) in near-constant time for sparse networks, providing scalable analysis of higher-order network structure. Collectively, RSNet offers a versatile tool for statistically reliable and interpretable network inference in high-dimensional data.

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity of and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine learning (ML) models to yield insightful analytical outcomes. This review examines the integration of machine learning into natural product chemistry from 2015 to 2023, highlighting its potential to overcome the inherent limitations of traditional chemical techniques. We present a structured approach that matches specific natural product challenges—such as component determination, concentration prediction, and classification—with suitable ML models, including regression, classification, and dimension reduction methods. Our objective is to illustrate how ML pipelines, from data preprocessing to model evaluation, enhance both qualitative and quantitative analyses, providing a comprehensive framework, with the potential catalyze a transformation in the field of natural product analysis.