Translational Bridge

We serve as a critical bridge connecting the power of AI and machine learning with the practical needs of researchers and clinicians. This translational focus ensures that cutting-edge computational methods don’t remain isolated in the AI world but become accessible, usable tools that accelerate biomedical discovery and improve patient outcomes. We actively work to close the gap between what’s possible with AI and what’s practical in research labs and clinical settings.

Bridging the Gap

Understanding Both Worlds

AI/ML Expertise: Deep understanding of machine learning methods, their capabilities and limitations
Biological Domain Knowledge: Comprehensive grasp of genomics, disease biology, and clinical needs
Practical Constraints: Awareness of real-world limitations in clinical and research settings

Making AI Accessible

User-Friendly Interfaces: Web-based tools requiring no programming expertise
Interpretable Results: Clear explanations that bridge technical and clinical language
Actionable Outputs: Results that directly inform research decisions and clinical care
Training & Education: Workshops and documentation to empower users

Two-Way Communication

Clinician Feedback: Continuous input from end-users shapes tool development
Research Integration: Embedding tools into existing laboratory workflows
Iterative Improvement: Rapid cycles of development, testing, and refinement based on real-world use

Key Research Areas

AI-Powered Rare Disease Diagnosis

AI-MARRVEL (AIM)

Our flagship machine learning system, published in NEJM AI, helps clinicians prioritize potentially causative genetic variants for Mendelian disorders. Diagnosing rare genetic diseases is often a multi-year “diagnostic odyssey” - AI-MARRVEL significantly accelerates this process by:

Variant Prioritization: Ranking genetic variants based on their likelihood of causing disease
Evidence Integration: Combining diverse data sources including population genetics, functional predictions, model organism data, and clinical phenotypes
Clinical Phenotype Matching: Leveraging HPO (Human Phenotype Ontology) terms to match patient symptoms with known disease profiles
Literature Mining: Automatically extracting relevant information from millions of scientific publications

Autism Research & Diagnosis

Supported by grants from the NIH Autism Data Science Initiative and the Silicon Valley Community Foundation, our autism research program focuses on:

Data-Driven Discovery

Large-Scale Data Analysis: Mining autism genomic databases to identify risk genes and pathways
Molecular Subtyping: Defining biologically meaningful autism subtypes based on molecular signatures
Biomarker Development: Identifying molecular markers for early diagnosis and treatment response

Mechanistic Understanding

Gene Network Analysis: Understanding how autism risk genes interact and contribute to disease
Cellular Pathways: Identifying disrupted biological pathways that can be therapeutic targets
Brain Development: Studying how genetic variants affect neurodevelopment

Undiagnosed Disease Programs

We collaborate with clinical genetics programs to solve the most challenging diagnostic cases:

Whole Genome/Exome Analysis: Comprehensive analysis of patient genetic data
Novel Gene Discovery: Identifying previously unknown disease genes
Variant Interpretation: Determining the pathogenicity of rare variants
Family Studies: Analyzing inheritance patterns and segregation

Neurological Disease Applications

Neurodegenerative Diseases

Cellular Heterogeneity: Defining how different cell types are affected in neurodegeneration
Disease Progression: Modeling molecular changes over disease course
Therapeutic Target Identification: Finding druggable pathways and proteins

Neurodevelopmental Disorders

Gene Discovery: Identifying new neurodevelopmental disease genes
Genotype-Phenotype Correlations: Understanding how specific mutations lead to clinical features
Functional Validation: Testing disease mechanisms in model systems

Clinical Impact

Diagnostic Rate Improvement

Our tools have helped increase diagnostic rates for rare diseases, ending diagnostic odysseys for numerous families and enabling precision medicine approaches.

Clinical Decision Support

We provide clinicians with interpretable, evidence-based recommendations that integrate seamlessly into existing clinical workflows.

Personalized Medicine

By understanding the molecular basis of each patient’s condition, we enable tailored treatment approaches and inform prognosis.

Translational Research Infrastructure

Clinical Collaborations

Texas Children’s Hospital: Close collaboration with clinical genetics and neurology departments
Baylor College of Medicine: Integration with genetic testing laboratories
Undiagnosed Diseases Network: Participation in national collaborative efforts
International Partnerships: Contributing to global rare disease initiatives

Patient-Centered Approach

Our research is guided by the needs of patients and families affected by rare genetic disorders. We actively engage with patient advocacy groups and incorporate patient perspectives into our tool development.

Regulatory & Clinical Validation

We work toward clinical-grade tools that meet regulatory standards and undergo rigorous validation in clinical settings.

Real-World Implementation

Web-Based Platforms

User-friendly interfaces that require no programming expertise, making advanced AI accessible to all clinicians.

Secure Data Handling

HIPAA-compliant infrastructure ensuring patient privacy and data security.

Continuous Improvement

Ongoing updates incorporating new scientific knowledge, clinical feedback, and improved machine learning models.

Future Directions

Expanded Disease Coverage: Extending our methods to broader categories of genetic disorders
Therapeutic Predictions: Using AI to suggest potential treatments based on molecular mechanisms
Real-Time Clinical Integration: Embedding our tools directly into electronic health records
Multi-Modal Data: Incorporating imaging, clinical notes, and other data types for comprehensive diagnosis