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dianalaplace/README.md

Diana Lysenko

ML in Life Sciences · Bioinformatics

I build machine learning pipelines for genomic and transcriptomic data — from raw variant calls to biological insight. My work sits at the intersection of computational biology and applied ML, with a focus on rare disease genetics and musculoskeletal conditions.

Current Projects

Repository Description Stack
scoliosis-variant-ml Pathogenic variant signatures in scoliosis-associated genes Python, scikit-learn, UMAP
ivd-ecm-regeneration-ml ECM-regenerative gene signatures in IVD degeneration via scRNA-seq Python, Scanpy
tcga-hnsc-transcriptomics-genetics Bulk RNA-seq & somatic variant analysis of TCGA-HNSC Python, Jupyter

Skills

Python R Jupyter Git scikit-learn Pandas NumPy

Interests

Computational genomics · Rare disease genetics · Unsupervised learning on omics data · Single-cell analysis

Open to collaborations in computational biology and ML for healthcare.

Pinned Loading

  1. tcga-hnsc-transcriptomics-genetics tcga-hnsc-transcriptomics-genetics Public

    TCGA-HNSC bulk RNA-seq analysis and VCF filtering

    Jupyter Notebook

  2. ivd-ecm-regeneration-ml ivd-ecm-regeneration-ml Public

    ML-driven identification of ECM-regenerative gene signatures in human intervertebral disc degeneration via multi-dataset scRNA-seq integration. Supervised classification of anabolic vs catabolic ce…