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Kerstin Lenhof

Professor @ Universitätsmedizin Göttingen & CAIMed (Lower Saxony Center for AI & Causal Methods in Medicine)

Teaching:

  • Selected lectures for Personalized Medicine (lecture series by Ulrich Sax)
    Georg-August-Universität Göttingen
    Lecture titles: Bioinformatics - The role of bioinformatics in gene sequencing and interpretation of genetic information; Clinical prognosis with biomarkers
  • Selected lectures for Bioinformatics in Systems Biology (lecture by Tim Beißbarth)
    Georg-August-Universität Göttingen
    Lecture titles: Dimension Reduction & Single Cell Methods; Machine learning with networks

  • Computational Biomedicine (shared with Michael Altenbuchinger)
    Georg-August-Universität Göttingen
  • Selected lecture for Genome Science (lecture series by the International Max Planck Research School for Genome Science)
    Georg-August-Universität Göttingen
    Title of lecture: Machine learning
  • Selected lecture for Bioinformatics and its areas of application (lecture series by Jan de Vries)
    Georg-August-Universität Göttingen
    Title of lecture: How to train your model
 - Trustworthy machine learning for personalized oncology
  • Selected lectures for Angewandte Bioinformatik (lecture by Tim Beißbarth/Martin Haubrock)
    Georg-August-Universität Göttingen

  • Seminar supervision for Computational Biomarker Discovery
    Saarland University

  • Teaching assistant for Bioinformatics I
    Saarland University
  • Seminar supervision for Computational Biomarker Discovery
    Saarland University

  • Teaching assistant for Bioinformatics II
    Saarland University
  • Seminar supervision for Machine Learning Applications in Bioinformatics
    Saarland University

  • Seminar supervision for Machine Learning Applications in Bioinformatics
    Saarland University

  • Seminar supervision for Machine Learning Applications in Bioinformatics
    Saarland University

  • Seminar supervision for Computational Biomarker Discovery
    Saarland University

  • Seminar supervision for Computational Biomarker Discovery
    Saarland University

  • Teaching assistant for Bioinformatics I
    Saarland University
  • Seminar supervision for Bioinformatic Approaches for Personalized Cancer Treatment
    Saarland University

  • Teaching assistant for Bioinformatics II
    Saarland University
  • Seminar supervision for Bioinformatic Approaches for Personalized Cancer Treatment
    Saarland University

  • Seminar supervision for Project Seminar: Machine Learning
    Saarland University

  • Tutor for Bioinformatics II
    Saarland University

  • Tutor for Bioinformatics I
    Saarland University
  • Tutor for Preparatory Course in Mathematics for Computer Scientists
    Saarland University

  • Tutor for Mathematics for Computer Scientists II
    Saarland University

  • Tutor for Mathematics for Computer Scientists I
    Saarland University
  • Tutor for Preparatory Course in Mathematics for Computer Scientists
    Saarland University

Thesis/Project Advisor:

Student research assistants:

  • Lisa-Marie Rolli
    (2022 - 2024 (June))
  • Sanaa Sangien
    (November 2025 - May 2026)
  • Four interns from AI Safety Saarland: Daniya Niazi, Muhammad Usman Chaudhary, Leonard Schmidt, Gilles Dongmo
    (November 2025 - February 2026)

Research projects (lab rotations):

  • Youmna Abboud
    (January 2025 - April 2025)
  • Aly Hessam
    (May - July 2026)

Master theses:

  • TBA
    Loulwah Aranout (2026)
  • TBA
    Bilal Kachir (2026)
  • TBA
    Carolin Lafeld (2026)
  • TBA
    Saliha Seray Yagci (2026)
  • Benchmarking and Improving Domain Adaptation Models for Single-Cell Drug Response Prediction
    Michael Bohl (2026)
  • Diagnose Your Model: A Framework for Realistic ML in Drug Discovery
    Zyad Barghouth (2026)
  • Benchmarking of Deep Learning Approaches for Anti-Cancer Drug Sensitivity Prediction
    Lutz Herrmann (2025)
  • Increasing Trust in ML-based Drug Sensitivity Prediction
    Lisa-Marie Rolli (2024), won Günter Hotz medal by FdSI
  • 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:

  • TBA
    Artemiy Tishchenko (2026)
  • Drug Synergy and Sensitivity Prediction using Feed Forward Neural Networks
    Lutz Herrmann (2024)
  • Enrichment Analyses for the Identification of Drug Response Markers in Cancer Cell Lines
    F. S. (2024)
  • 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)