The Challenge
Cancer is fundamentally a disease of the genome, driven by a complex array of genetic alterations that vary widely between patients. The advent of high-throughput sequencing technologies has led to an explosion of genomic data, presenting both opportunities and challenges.
While we now have unprecedented access to genomic information, extracting meaningful clinical insights from this data remains difficult. Challenges include interpreting the functional impact of genetic variants, understanding complex gene-gene interactions, integrating multi-omics data types, and translating genomic findings into actionable therapeutic decisions.
Our Approach
Our research team is applying cutting-edge AI and machine learning techniques to address these challenges in cancer genomics. Our multifaceted approach includes:
- Developing deep learning models that can predict the functional consequences of genetic alterations
- Creating algorithms that identify patterns in multi-omics data to discover new cancer subtypes
- Building systems that integrate genomic data with clinical information to guide treatment decisions
- Applying natural language processing to mine biomedical literature and integrate published knowledge with genomic analyses
- Constructing comprehensive biological network models that capture complex interactions between genes, proteins, and cellular pathways
Multi-omics Integration
Combining genomic, transcriptomic, proteomic, and epigenomic data to create a comprehensive picture of tumor biology and identify new therapeutic targets.
Variant Interpretation
Using deep learning to predict the pathogenicity and functional impact of genetic variants, particularly novel variants of uncertain significance.
Network Biology
Applying graph neural networks to model complex biological interactions and identify key regulatory nodes and potential drug targets.
Biomarker Discovery
Identifying genomic signatures that predict treatment response, disease progression, and patient outcomes.
Current Research Projects
Synthetic Lethality Prediction
We're developing deep learning models to predict synthetic lethal interactions in cancer cells, which occur when the simultaneous perturbation of two genes leads to cell death, while disruption of either gene alone does not. These predictions can uncover novel therapeutic strategies for targeting cancers with specific genetic alterations.
Cancer Neoantigen Prediction
This project focuses on using AI to predict cancer-specific neoantigens—altered proteins expressed by tumor cells that can be recognized by the immune system. Our algorithms integrate genomic, transcriptomic, and proteomic data to identify promising neoantigen targets for personalized cancer vaccines and immunotherapies.
Tumor Evolution Modeling
We're applying probabilistic models to track the evolutionary dynamics of cancer genomes over time, particularly in response to treatment pressures. This approach helps identify mechanisms of resistance and inform adaptive treatment strategies that anticipate tumor evolution.
Integrative Multi-omics Clustering
Our team is developing novel algorithms for integrating multiple types of molecular data to identify previously unrecognized cancer subtypes with distinct biological characteristics and clinical behaviors. This work aims to refine cancer classification and enable more precise treatment approaches.
Technological Innovations
Our genomic data research incorporates several innovative technological approaches:
- Graph neural networks: For modeling complex biological relationships and predicting the effects of genetic perturbations
- Transformer-based models: Adapted from natural language processing to understand the "language" of genomic sequences
- Multi-modal learning: Integrating diverse data types with different structures and dimensions
- Federated genomics: Enabling collaborative research across institutions while preserving data privacy
- Explainable AI frameworks: Providing interpretable insights into model predictions that can be validated by domain experts
Translational Impact
Our genomic data research aims to have direct impact on cancer care through:
Target Discovery
Identifying novel therapeutic targets and drug candidates for cancers with specific genetic alterations.
Precision Medicine
Enabling more precise matching of patients to optimal therapies based on their tumor's genetic profile.
Resistance Mechanisms
Uncovering genomic mechanisms of treatment resistance and strategies to overcome them.
Early Detection
Developing genomic biomarkers for early cancer detection in blood and other non-invasive samples.
Future Directions
As our research progresses, we plan to explore several exciting directions:
- Integrating spatial genomics data to understand how the physical organization of genes within the nucleus affects cancer development
- Developing models that can predict the effects of combinations of targeted therapies based on a tumor's genomic profile
- Creating systems that integrate real-time genomic monitoring to track treatment response and tumor evolution
- Extending our analytic frameworks to encompass the role of the microbiome in cancer development and treatment response
- Building computational platforms that democratize access to advanced genomic analysis capabilities for researchers worldwide
Collaborations and Partnerships
We're actively seeking partnerships with:
Cancer Centers
For access to patient samples and clinical data to validate our genomic models
Pharmaceutical Companies
To translate our genomic insights into new drug development programs
Genomics Technology Companies
To integrate our AI tools with cutting-edge sequencing and analysis platforms
Research Background
This research area contributes to the growing body of knowledge in AI-powered cancer research. We're currently developing foundational work in this space.
Related Research Areas
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