Bioinformatics · cancer genomics · interpretable ML
I build computational methods for biological discovery.
I’m a PhD student at Rice University developing bioinformatics tools for single-cell and epigenomic data, studying cancer mutations, and making machine-learning results more interpretable.
1 entry batch correction research
batch correction cancer epigenomics cancer microbiome colorectal cancer dna methylation interpretable nmf single-cell rna-seq small rna sequencing tumor purity Line weight = shared entries · hover to trace
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New research notes and writing, ordered by the date they were last revised.
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Research Recovering tumor-intrinsic DNA methylation signals from bulk cancer epigenomes MONTE estimates tumor purity and corrects DNA methylation at single-CpG resolution, recovering tumor-intrinsic regulatory signals from bulk cancer samples. Research Disentangling condition-specific signals from complex sequencing data ALPINE separates condition-associated structure from shared and confounding variation while directly prioritizing genes linked to the condition of interest. Research Profiling the cancer-associated microbiome from small RNA sequencing data An analytical framework recovers microbial signals from host microRNA sequencing data to study microbiome–tumor relationships when direct microbial sampling is unavailable.