Research Areas

Using bioinformatic and genomics approaches across basic science research problems and in translational medicine.

Our Approach

The Georgakopoulos-Soares Lab combines computational biology, genomics, and machine learning to understand the complex patterns of mutations that drive cancer development and progression.

We develop innovative computational methods to analyze large-scale genomic datasets, with the goal of identifying new biomarkers, therapeutic targets, and insights into cancer biology.

Our interdisciplinary approach brings together expertise from computer science, statistics, molecular biology, and clinical oncology to address key challenges in cancer research.

Genomic Analyses

We study how DNA sequence composition and non-B DNA structures shape genomic instability, evolution, and cancer.

Machine Learning

Developing algorithms that surface actionable insights from complex data.

Translational Research

We develop algorithms for diagnosing, monitoring, and treating human diseases.

Tool Development

Creating open resources for the scientific and medical community.

Research Focus Areas

Genomics Research

Key Projects:

  • Understanding the contribution of non-B DNA to genomic instability
  • Applying cutting-edge ML techniques, and interpretable AI
  • Leveraging k-mers to develop novel tools and methods
  • Developing resources and tools for the scientific community
Genomic AnalysisPattern RecognitionTool & Database Development

Genomic Instability and Cancer Evolution

Key Projects:

  • Cataloguing genomic instability patterns across tumors
  • Relating genomic instability to immunotherapy response
  • Tracking tumor evolution via instability markers
  • Identifying synthetic lethal partners of instability pathways
Tumor EvolutionGenomic InstabilityTreatment Resistance

Computational Methods for Cancer Genomics

Key Projects:

  • Multi-omics integration with machine learning
  • Frameworks for mutational signature analysis
  • Algorithms for discovering cancer driver mutations
  • Deep learning models for cancer classification and prognosis
Machine LearningAlgorithm DevelopmentData Integration

Translational Cancer Genomics

Key Projects:

  • Biomarkers for early cancer detection
  • Predictive markers for immunotherapy response
  • Prognostic and treatment outcome models
  • ctDNA monitoring for treatment response
Biomarker DiscoveryPrecision MedicineClinical Translation

Our Methods & Technologies

Computational Genomics

Advanced pipelines for whole-genome, whole-exome, and transcriptomic data analysis.

Machine Learning

Supervised, unsupervised, and deep learning models tailored to genomic signals.

Data Integration

Combining genomic, epigenomic, transcriptomic, and clinical data for holistic insights.

Statistical Modeling

Robust statistical frameworks for mutational processes and clinical associations.

Software & Resources