Michael Lamkin

This study improves gene expression prediction models by incorporating tandem repeats, showing significant performance gains and identifying nonlinear effects on gene expression.

This paper introduces a haplotype-based fine-mapping method called happler to identify causal variants for complex traits, showing improved performance over traditional methods in detecting haplotype effects.

This study evaluates how promoter proximal short tandem repeats (STRs) affect gene expression using MPRA, identifying key factors like repeat unit sequence and copy number.

This study identifies de novo tandem repeat mutations in ASD probands and shows they are more prevalent, larger, and evolutionarily deleterious compared to unaffected siblings.

The paper uses deep neural networks to identify context-specific determinants of transcription factor binding affinity and shows improved prediction performance over existing methods.

This paper introduces Tulip, a toolkit for simulating ChIP-seq data to evaluate analyses like peak calling and differential binding, enabling power analysis and benchmarking.