Rigorous Validation & Data Science
Ensuring AI-driven discoveries are robust, reproducible, and biologically meaningful through comprehensive validation frameworks.
Rigorous Validation & Data Science
We are committed to ensuring that AI-driven findings are not just computationally impressive, but biologically robust and clinically actionable. Our validation framework combines large-scale data analysis, experimental validation, and rigorous statistical testing to ensure reproducibility and reliability. This work is supported by major grants including the NIH Autism Data Science Initiative (ADSI) and the Silicon Valley Community Foundation, reflecting the importance of rigorous, validated approaches in advancing biomedical discovery.
Validation Frameworks
Computational Validation
- Cross-Validation & Benchmarking: Rigorous testing against gold-standard datasets
- Replication Studies: Validating findings across independent cohorts
- Robustness Analysis: Ensuring predictions are stable across different conditions
- Statistical Rigor: Multiple testing correction and proper statistical frameworks
Experimental Validation
- Functional Genomics: Testing predictions using CRISPR and other perturbation methods
- Model Organisms: Validating human disease genes in Drosophila and other systems
- Multi-Omics Confirmation: Verifying findings across genomic, transcriptomic, and proteomic layers
Clinical Validation
- Patient Cohort Studies: Testing predictions in real patient populations
- Longitudinal Data: Validating temporal predictions across disease progression
- Clinical Outcome Correlation: Ensuring predictions align with clinical observations
Key Research Areas
Autism Data Science (NIH ADSI & SVCF Supported)
Supported by the NIH Autism Data Science Initiative and the Silicon Valley Community Foundation, our autism research program emphasizes rigorous, data-driven approaches:
- Large-Scale Genomic Analysis: Systematic analysis of autism genomic databases with strict quality control
- Reproducible Pipelines: Standardized, well-documented analysis workflows
- Cross-Study Validation: Confirming findings across multiple independent autism cohorts
- Biological Mechanism Validation: Testing autism risk gene functions experimentally
- Phenotype-Genotype Correlation: Rigorous statistical analysis of clinical-molecular relationships
Transcriptional Regulation
- Transcription Factor Networks: Mapping regulatory circuits that control gene expression
- Chromatin Accessibility: Analyzing open chromatin regions and their role in gene regulation
- Enhancer-Promoter Interactions: Understanding long-range regulatory elements
- RNA Regulation: Investigating post-transcriptional control mechanisms including microRNAs and RNA-binding proteins
Single-Cell Genomics
- Cell Type Identification: Defining cellular heterogeneity in complex tissues
- Developmental Trajectories: Reconstructing cell state transitions during development and disease
- Spatial Transcriptomics: Integrating gene expression with spatial context in tissues
- Single-Cell Multi-Omics: Profiling multiple molecular layers simultaneously in individual cells
Gene Function & Networks
- Gene Regulatory Networks: Inferring causal relationships between genes
- Protein-Protein Interaction Networks: Mapping physical and functional interactions
- Pathway Analysis: Identifying perturbed biological pathways in disease
- Gene Prioritization: Ranking candidate disease genes based on network properties
Multi-Omics Data Analysis
- Genomics: Analyzing DNA variation, structural variants, and genome architecture
- Transcriptomics: Profiling gene expression across conditions, tissues, and cell types
- Epigenomics: Examining DNA methylation, histone modifications, and chromatin states
- Proteomics: Integrating protein abundance and post-translational modifications
Experimental Validation
CRISPR Screening
- Pooled CRISPR Screens: Systematic perturbation of genes to identify functional relationships
- CRISPRcloud: Our cloud-based platform for analyzing CRISPR screening data
- Screen Design & Analysis: Optimizing experimental design and computational pipelines
Model Organisms
- Drosophila Studies: Leveraging the power of fly genetics for functional validation
- Cross-Species Conservation: Identifying evolutionarily conserved mechanisms
- Phenotypic Characterization: Detailed behavioral and molecular phenotyping
Neurological Disease Focus
Autism Spectrum Disorders
Investigating molecular mechanisms underlying autism, supported by grants from the NIH Autism Data Science Initiative and the Silicon Valley Community Foundation.
Rare Neurological Disorders
Studying genetic and molecular basis of rare neurodevelopmental and neurodegenerative conditions.
MeCP2 and Rett Syndrome
Understanding the role of MeCP2 and its regulatory networks in brain development and function.
Data Resources
MARRVEL Database
Our flagship resource integrating human genetic data with model organism information, facilitating functional annotation of the human genome and enabling researchers to leverage decades of model organism research for understanding human disease.
Transposable Element Analysis
Tools and methods for quantifying and analyzing transposable elements from RNA-seq data, revealing their roles in gene regulation and disease.