Novel Statistical Approaches in Biomarker Discovery, Analysis & Disease Screening
Conference
Category: International Statistical Institute
Proposal Description
Focus
Biomarkers or biological markers are essential in the prediction of diseases and are commonly used in basic and clinical research. The discovery and analysis of novel biomarkers have been a key interest for many researchers in screening for novel diseases. This session focuses on the advancements in the discovery and analysis of biomarkers along with developing diagnostic accuracy tools in detecting diseases.
Content
In healthcare and biomedical research, biomarkers are crucial for improving diagnostic accuracy, prognostic assessment, and treatment interventions, serving as measurable indicators of biological processes and disease states. However, traditional statistical methods often struggle with the complexity of biomarker data, prompting a surge in interest in novel statistical approaches tailored to this domain.
Our session aims to explore cutting-edge methodologies, including machine learning, Bayesian statistics, and high-dimensional data analysis. By convening experts across disciplines, we seek to foster collaboration, drive methodological innovation, and accelerate the translation of these approaches into clinical practice, ultimately advancing precision medicine and improving healthcare outcomes globally.
Timeliness
The session coincides with a pivotal moment in biomedical research and healthcare innovation. Advancements in high-throughput technologies and imaging have led to an exponential growth in biomarker data complexity. This underscores the urgent need for innovative statistical methodologies to handle such intricacies and translate insights into clinical applications. Additionally, global health challenges like COVID-19 have emphasized the importance of biomarkers in disease diagnosis, prognosis, and management, heightening interest in their discovery and analysis.
Recent strides in computational biology, artificial intelligence, and data science have transformed biomarker discovery. Leveraging cutting-edge statistical approaches, researchers can now analyze complex biological systems and identify novel biomarkers with unprecedented precision. The session will serve as a platform for showcasing these methodologies, sharing best practices, and fostering collaborations among experts from academia, industry, and healthcare.
Appeal
This session appeals to researchers, statisticians, and healthcare professionals seeking to stay at the forefront of biomarker discovery and analysis. By presenting novel statistical approaches tailored to current data challenges, the session promises practical insights and actionable strategies for advancing precision medicine and improving patient care.
Submissions
- Bayesian joint latent-class modeling with application to acute lymphoblastic leukemia maintenance study
- Biomarker discovery in population cohort studies of the human microbiome
- Covariate adjusted ROC surface regression and optimal cutoff estimation
- EHR drive prognostic models to assist disease screening
- Inference in longitudinal data analysis with terminating events