Exploring genomes, transcriptomes, and proteomes. Transforming complex omics data into insight with ML, scalable pipelines, and visualization.
Whole-genome analysis, variant calling, population genomics.
Differential expression, single-cell analysis, pathway enrichment.
Mass-spec data processing, protein quantification, network analysis.
Predictive models, feature selection, integration of multi-omics data.
Python, Bash, Snakemake, Nextflow, Docker, Kubernetes for reproducible workflows.
Interactive dashboards, heatmaps, clustering, network visualization.
Machine learning model integrating RNA-seq and clinical data to rank survival chances.
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