Ciencias de la vida

Proximal Individualized Functional Treatment Regimes

- Estimating individualized treatment regimes (ITRs) is fundamental in data-driven personalized decision-making problems, such as precision medicine. Most of the ITR literature either focuses on...

Bayesian Distilled Clustering for High-Dimensional Mixture Models

- Latent subgroup analysis is central to fields such as genomics, precision medicine, and social science, where the goal is to identify heterogeneous populations with distinct covariate structures or...

From Precision Medicine to Precision Education: A Vision for AI-Powered Student Digital Twins, Preventive Student Success, and Career-Aligned Academic Pathways

- Higher education remains largely reactive in its approach to student success. Institutions frequently identify academic problems only after students have failed courses, fallen behind in degree...

Evaluating the influence of treatment-effect heterogeneity on discrimination

- Analyzing the heterogeneity of treatment effects is crucial in personalized medicine to identify which patients will benefit from specific treatments. The performance of a conditional average...

Causal inference for N-of-1 trials

- The aim of personalized medicine is to tailor treatment decisions to individuals' characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting...

Backward Bayesian Outcome Weighted Learning

- A central objective of precision medicine is learning optimal dynamic treatment regimes (DTRs) from data. Classification-based methods, like outcome weighted learning (OWL) for single-stage and...

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

- As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs....

Multiple type I error concepts for clinical trials with overlapping populations

- The population-wise error rate (PWER) was introduced as a more liberal alternative to the family-wise error rate (FWER) for clinical trials with multiple, overlapping patient populations. These...

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

- Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine. We focus on the conditional average treatment effect (CATE), a...

SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

- Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous...

Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits

- Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and learned securely using only historical logged...

Neural Networks of Outcome Weighted Learning for Individualized Treatment Rules

- Individualized treatment rules (ITRs) formalize precision medicine by assigning treatments according to patient covariates, with the goal of maximizing expected clinical outcomes. Such rules are...

A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization

- Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains...

Improving Precision of RCT-Based CATE Estimation using Data Borrowing with Double Calibration

- Understanding how treatment effects vary across patient characteristics is essential for personalized medicine, yet randomized controlled trials (RCTs) are often underpowered to detect heterogeneous...

Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

- Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning....

Prior-informed conditional Gaussian graphical models: an application to protein interaction network reconstruction

- Protein-protein interaction (PPI) networks, estimated from high-throughput omics data, foster biomarker discovery and precision medicine. Gaussian graphical models (GGMs) offer a principled...

A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data

- Identifying patients who are likely to benefit from a treatment is central to precision medicine and can guide follow-up trials, enrichment designs, and individualized decisions. Although randomized...

Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture

- Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR...

Competing Accelerated Failure Time Models for Multiple Concurrent Failure Mechanisms

- The rising prevalence of complex diseases characterised by multiple coexisting and interacting etiological processes poses critical challenges for survival analysis and precision medicine,...

SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis

- Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor microenvironments and guiding...

OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology

- Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrier to forecasting. We introduce OphthaDT, an...