Aug 2026· Applied Informatics· Vol 7, pp. 304· 0 citations· 26 references
TL;DR
Empirical evaluations on eight public real-world biomedical HDLSS gene-expression datasets demonstrate that GINet-DGC achieves competitive and stable performance against 17 baselines, and support the effectiveness of the proposed framework within the evaluated public biomedical HDLSS benchmark setting.
Abstract
Learning from high-dimensional, low-sample-size (HDLSS) data remains a persistent challenge in machine learning, as models must infer reliable patterns from limited observations while handling an excessive number of variables—a scenario particularly prevalent in biomedical applications. Such data structures render predictive modeling highly vulnerable to erratic optimization and overfitting. To address this challenge, we propose the Global Interaction Network with Dynamic Generalization Control (GINet-DGC), an artificial intelligence (AI) framework that integrates feature-wise structural priors with dynamic generalization monitoring. Rather than directly learning an unconstrained first-layer weight matrix, GINet-DGC generates task-specific weights from multi-view feature descriptors, encompassing latent semantic, global distributional, local topological, and hierarchical representations. This structure-constrained weight generation strategy effectively narrows the feature-interaction search space and acts as an inductive regularizer against noise and redundant molecular features. Furthermore, we introduce an Overfitting-aware Index (OFI) to monitor the training trajectory and effectively identify the generalization saturation point for adaptive termination. Empirical evaluations on eight public real-world biomedical HDLSS gene-expression datasets, using a repeated stratified 5 × 5 cross-validation protocol, demonstrate that GINet-DGC achieves competitive and stable performance against 17 baselines. These findings support the effectiveness of the proposed framework within the evaluated public biomedical HDLSS benchmark setting.
This work proposes post-pretrained lasso selective inference (PPL-SI), a novel selective inference method designed to provide statistically valid p values for the pretrained lasso that reliably controls false discoveries and significantly improves the detection of biologically relevant features compared to traditional approaches.
Cao Huyen My, Nguyen Vu Khai Tam, Vo Nguyen Le Duy· Statistics and computing· 0 citations
Results suggest that PLAT provides an effective and interpretable framework for high-dimensional transcriptomic classification and functional enrichment analyses consistently highlighted biological processes and disease pathways associated with breast cancer, supporting the biological relevance of the learned latent representations.
Kamal Elatifi, Nicolas Jäger Gallego, Á. Sánchez-Pla et al.· Applied Sciences· 0 citations
High-dimensional data and multicollinearity present major challenges in regression analysis, often causing overfitting and unstable coefficient estimates. The Elastic-net model, which combines (Lasso) and (Ridge) regularization, offers a robust solution by enhancing feature selection and handling multicollinearity. This study improves Elastic-net by integrating two optimization techniques: Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam). The SGD Elastic-net model accelerates convergence and boosts computational efficiency through mini-batch updates, while the Adam Elastic-net model incorporates adaptive learning rates and momentum to enhance stability and performance, especially with noisy or sparse data. Simulated data analysis showed that both Adam and SGD Elastic-net models produced more precise coefficient estimates with narrower confidence intervals, improving interpretability and robustness. A real-world application on a prostate cancer dataset further confirmed these advantages. Diagnostic plots validated the assumptions of linearity, normality, and homoscedasticity, supporting model reliability. Overall, the Adam and SGD Elastic-net approaches achieved faster convergence and lower residual errors, making them highly effective for high-dimensional datasets facing multicollinearity.
In biomedical research, multiple types of high-dimensional data, such as genomic, transcriptomic, proteomic, and metabolomic data, are increasingly collected from the same subjects. Integrating these multiple data views can improve prediction by exploiting shared or complementary information across the views. Cooperative learning provides an agreement-based framework for multiview supervised learning by encouraging predictions obtained from individual views to be similar. However, the original framework assumes independent observations and therefore does not account for clustered structures, such as repeated measurements obtained from the same subject. To address this limitation, we propose Cooperative Learning with a penalized Linear Mixed Model (CL-pLMM) for high-dimensional multiview data with a clustered structure. CL-pLMM replaces the ordinary prediction loss in cooperative learning with a covariance-weighted loss that accounts for within-cluster dependence, while retaining the agreement penalty between views and a Lasso penalty for variable selection. We further show that its objective function can be represented as a penalized linear mixed-effects model applied to augmented data, allowing existing estimation procedures to be used. The performance of CL-pLMM is evaluated through simulation studies under various signal and dependence settings and an application to longitudinal proteomic and metabolomic data for predicting the time to spontaneous labor.
The survkl software is introduced, which enables the incorporation of external summary-level information with newly collected time-to-event data to support more robust and accurate predictions in survival analysis.
Yu-Bo Shao, Lingfeng Luo, Xiao-Han Liu et al.· Bioinformatics Advances· 0 citations
Constructing interpretable disease models from longitudinal omics data is a central challenge in precision medicine. The goal is a low-dimensional representation in which a patient’s position encodes their molecular state and clinical severity, and along which disease progression can be read directly. Existing dimensionality reduction methods (e.g., UMAP, Variational Autoencoders) fall short of this goal: they optimize a single generic objective and are blind to clinical labels and to the temporal ordering of measurements. Consequently, trajectory inference is typically applied after the fact to an embedding that was never optimized to reveal progression, decoupling the representation from disease dynamics. Manifold learning offers a natural route to such representations, and we build on Locally Linear Embedding (LLE) to preserve the local geometry of the omics data (i.e., keeping molecularly similar patients close together in the low-dimensional space). Geometry alone, however, yields a space that is faithful to molecular similarity yet uninformative about clinical severity and progression. We therefore recast the problem as multi-task learning: MT-LLE jointly optimizes five objectives: geometric reconstruction, supervised organization by clinical stage, embedding and phenotype forecasting, and clustering. Because naively combining such heterogeneous objectives induces gradient conflicts that distort the molecular geometry, an embedded reinforcement learning agent dynamically schedules their weights during training, establishing global geometry before refining clinical boundaries. Across two independent Chronic Obstructive Pulmonary Disease (COPD) cohorts (SPIROMICS and COPDGene), MT-LLE deliberately relaxes exact geometric reconstruction, by a modest margin, in exchange for substantial gains in clinical structure. On held-out patients, a linear model reads disease severity (GOLD stage, 0–4) from the MT-LLE embedding 35–40% more accurately than from standard dimensionality reduction (0.53 vs. 0.38 F1-Macro). The gap is starker for progression: forecasting a patient’s next-visit severity from their trajectory reaches 0.38 F1-Macro, while unsupervised baselines sit near zero (0.06–0.09 F1-Macro), a temporal signal those methods fail to capture. To test whether the reinforcement learning agent earns its place, we compared it against a fixed schedule that imposes the same ordering of objectives but cannot adapt during training; the learned agent outperforms it by 13–20% across clinical metrics, showing the gains come from adapting the weights to how training unfolds, not merely from ordering the objectives correctly, and at no cost to geometric fidelity. Beyond these quantitative gains, the manifold supports complementary analyses that surface structure invisible to standard staging: static phenotyping isolates subjects with active molecular pathology despite preserved lung function; trajectory inference maps two mechanistically distinct progression axes (inflammatory fibrosis and pan-immune activation); and kinematic analysis of each patient’s speed and acceleration identifies subjects whose molecular trajectories accelerate ahead of detectable spirometric decline.
S. Hussein, I. Konigsberg, K. Kechris et al.· bioRxiv· 0 citations
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