Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations [PDF]
arXiv, 2023 During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker-mixed populations, later trials are more likely to enrol biomarker-positive patients alone, thus leading to trials of the same treatment investigated in different populations.
arxiv
Decision Tree for Protein Biomarker Selection for Clinical Applications [PDF]
arXiv, 2023 Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure as biomarker candidates cannot be validated or translated to immunoassays.
arxiv
Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso [PDF]
arXiv, 2022 In clinical trials, identification of prognostic and predictive biomarkers is essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment.
arxiv
Nonparametric empirical Bayes biomarker imputation and estimation [PDF]
arXiv, 2023 Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down-stream analysis can be conducted with modern ...
arxiv
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma [PDF]
arXiv, 2023 Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported.
arxiv
The Future will be Different than Today: Model Evaluation Considerations when Developing Translational Clinical Biomarker [PDF]
arXiv, 2021 Finding translational biomarkers stands center stage of the future of personalized medicine in healthcare. We observed notable challenges in identifying robust biomarkers as some with great performance in one scenario often fail to perform well in new trials (e.g. different population, indications). With rapid development in the clinical trial world (e.
arxiv
Preventing dataset shift from breaking machine-learning biomarkers [PDF]
arXiv, 2021 Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine
arxiv
Extracting Digital Biomarkers for Unobtrusive Stress State Screening from Multimodal Wearable Data [PDF]
arXiv, 2023 With the development of wearable technologies, a new kind of healthcare data has become valuable as medical information. These data provide meaningful information regarding an individual's physiological and psychological states, such as activity level, mood, stress, and cognitive health.
arxiv
Joint Modeling of Biomarker Cascades Along An Unobserved Disease Progression with Differentiate Covariate Effects: An Application in Alzheimer's Disease [PDF]
arXiv, 2023 Alzheimer's Disease (AD) research has shifted to focus on biomarker trajectories and their potential use in understanding the underlying AD-related pathological process. A conceptual framework was proposed in modern AD research that hypothesized biomarker cascades as a result of underlying AD pathology.
arxiv
Clinical Contrastive Learning for Biomarker Detection [PDF]
NeurIPS 2022 Workshop: Self-Supervised Learning - Theory and
Practice, 2022 This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the medical field, there exists a variety of labels for data that serve different purposes at different stages of a diagnostic and treatment process.
arxiv