Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host–microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Summary: In non-small cell lung cancer (NSCLC) clinical trials, tumor burden (TB) is a key longitudinal biomarker for assessing treatment effects. Typically, standard-of-care (SOC) therapies and some novel interventions initially decrease TB; however, many patients subsequently experience an increase—indicating disease progression—while others show a continuous decline. In patients with an eventual TB increase, the change point marks the onset of progression and must occur before the time of the event. To capture these dynamics, we propose a cure-rate joint model that integrates time-to-event and longitudinal TB data and distinguishes change-point and non-change-point trajectory groups. For the change-point group, our approach flexibly estimates an individualized change point by leveraging time-to-event information. We use a Monte Carlo Expectation-Maximization (MCEM) algorithm for efficient parameter estimation. Simulation studies show that our model can recover diverse disease progression patterns and yield robust marginal TB outcome estimates while accommodating censoring complexity. When applied to a Phase 3 NSCLC trial comparing cemiplimab monotherapy to SOC, the treatment group demonstrates delayed tumor regrowth and consistently lower TB over time, highlighting the clinical utility of our approach. The implementation code is publicly available on https://github.com/quyixiang/JoCuR.

Background The integration of high-dimensional and multi-modal biomarker data remains a central challenge in precision medicine, hindered by noise, weak individual signals, and heterogeneous data structures. Methods We propose AdaMixNet, an adaptive mixed-effects deep learning framework that unifies nonlinear fixed-effects modeling with kernel-based random-effects estimation to robustly and accurately predict complex disease outcomes. By leveraging feature screening to distinguish sparse, high-impact biomarkers from dense, low-signal features, AdaMixNet captures both strong and subtle biological effects across diverse data modalities. Results Through comprehensive simulations and applications to two large-scale real cohorts, i.e., METABRIC (breast cancer) and ADNI (Alzheimer’s disease), AdaMixNet shows robust overall performance, often outperforming strong machine-learning and statistical baselines in simulation and achieving competitive or best results for several real-data outcomes while maintaining good performance across sample sizes from 1000 to 20,000. Conclusions AdaMixNet offers a generalizable and interpretable framework for integrating high-dimensional and multi-modal omics profiles with low-dimensional clinical data, accelerating the translation of molecular insights into clinical applications.