Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis

Area of Science:

• Genomic Medicine
• Computational Biology
• Bioinformatics

Background:

• Precision medicine offers personalized treatments for complex diseases like cancer and COVID-19 by analyzing clinical, molecular, and genomic data.
• Integrating patient metabolomics and genetics with clinical data aids in identifying biomarkers for predisposition, diagnosis, prognosis, and treatment.

Purpose of the Study:

• To model clinical and multi-omics data for identifying biological pathways, risk factors, and actionable information for early disease detection and novel therapy development.
• To facilitate the mainstream implementation of precision medicine, moving beyond symptom-driven approaches towards predictive diagnostics and tailored treatments.
• To recommend automated implementation of machine learning (ML) and artificial intelligence (AI) for data aggregation, multifactor examination, and clinical decision support.

Main Methods:

• Quantitative phenotype measurements and variant evaluation using ACMG guidelines.
• Analysis of variant frequencies and autosomal recessive carriers with phenotype manifestation.
• Building and training machine-learning prognostic models for processing heterogeneous data and identifying high-risk rare variants.

Main Results:

• Identification of statistical patterns across millions of features in clinical and multi-omics data.
• Development of predictive models for medically relevant outcomes.
• Potential for earlier interventions and tailored therapies for improved patient care.

Conclusions:

• Automated ML and AI approaches are essential for aggregating multimodal data and multifactor examination.
• A knowledge base of clinical predictors is needed for effective decision support in precision medicine.
• Addressing ethical considerations is vital for the successful implementation of precision medicine.