Kerstin Lenhof

Teaching:

Thesis/Project Advisor:

Student research assistants:

• Lisa-Marie Rolli
  (2022 - 2024 (June))

Master theses:

• Increasing Trust in ML-based Drug Sensitivity Prediction
  Lisa-Marie Rolli (2024)
• Drug sensitivity prediction using biased random forests
  Afnan Sultan (2023)
• Drug sensitivity prediction using neural networks
  Lea Eckhart (2020)
• Causal inference of small regulatory modules
  G. S. (2020)
• VAIANA: a region-based functional annotation tool for genetic variants
  C. M. (2020)
• Evaluation of association measures based on cumulative entropy
  L. S. (2019)

Bachelor theses:

• Drug Synergy and Sensitivity Prediction using Feed Forward Neural Networks
  Lutz Herrmann (2024)
• < Work in progress >
  F. S.
• T-cell classification by application of neural networks to single-cell RNAseq data from blood cells
  J. G. (2024)
• Advanced high-throughput enrichment analysis with metabolomics data using GeneTrail3
  E. S. H. (2023)
• Anticancer drug sensitivity prediction based on drug and cell line similarities
  N. E. (2023)
• Conformal prediction on drug sensitivity data using random forest
  Lisa-Marie Rolli (2022), won best bachelor's thesis award by FdSI
• A multi-task formulation of the method for rule identification with multi-omics data (MERIDA)
  A. B. (2022)
• Identification of key transcriptional regulators using single cell data
  J. S. (2021)
• Integrating genomic lineage reconstruction of single cells into the GeneTrail3 workflow
  G. v. A. (2020)
• Inferring rule-based biomarkers for neuroblastoma
  B. A. (2019)
• Network-based integration of omics data for prioritizing cancer genes
  A. K. (2018)