Ly, L.-H.: Deciphering cellular heterogeneity by single-cell transcriptome analysis. Dissertation, xiii, 131 S. (2022)

Kleinert, P.: Computational interpretation of disease-causing, structural, and non-coding human genetic variants. Dissertation, 118 S. (2022)

Gajos, M.: Analysis of the determinants of Pol II pausing. Dissertation, vi, 125 S. (2022)

Abdullaev, E.: Dynamical Aspects of the Evolution of Segmental Duplications in the Human Genome. Dissertation, xiii, 129 S. (2022)

Gralinska, E.: Association Plots visualize cluster-specific genes from high-dimensional transcriptomics data. Dissertation, vi, 108 S. (2022)

Barel, G.: Network Propagation with Node Core for Genotype-Phenotype Associations and Module Identification. Dissertation, xii, 161 S. (2021)

Zehnder, T.: Computational Approaches for the Prediction of Gene Regulatory Elements and the Analysis of their Evolutionary Conservation. Dissertation, vii, 155 S. (2021)

Heller, D.: Structural variant calling using third-generation sequencing data. Dissertation, xiii, 139 S. (2021)

Moeinzadeh, M.: De novo and haplotype assembly of polyploid genomes. Dissertation, vii, 157 S. (2019)

Ramisch, A.: Enhancer Prediction Based on Epigenomic Data. Dissertation, iv, 186 S. (2019)

Pockrandt, C. M.: Approximate String Matching: Improving Data Structures and Algorithms. Dissertation, 175 S. (2019)

Steiger, E.: Efficient Sparse-Group Bayesian Feature Selection for Gene Network Reconstruction. Dissertation, Freie Universität, Berlin (2018)

Huska, M. R.: Using machine learning to predict and better understand DNA methylation and genomic enhancers. Dissertation, viii, 143 S., Freie Universität, Berlin (2018)

Helmuth, J.: Robust Normalization of Next Generation Sequencing Data. Dissertation, xii, 145 S. (2017)

Kopp, W.: Statistical methods for motif hit enrichment in DNA sequences. Dissertation, 216 S. (2017)

Heinrich, V.: Aspects of Quality Control for Next Generation Sequencing Data in Medical Genetics. Dissertation, vi, 137, XXVII pp S., Freie Universität, Berlin (2017)

Alavi, N.: An Unsupervised Learning Approach For Understanding Biases In Structural Variant Detection. Master (2022)

Sliwa, P.: Heuristic Approaches To Conditional Independence Test Using Partial Distance Correlation. Master (2020)

Despang, A.; Schöpflin, R.; Franke, M.; Ali, S.; Jerkovic, I.; Paliou, C.; Chan, W.-L.; Timmermann, B.; Wittler, L.; Vingron, M. et al.; Mundlos, S.; Ibrahim, D. M.: Functional dissection of TADs reveals non-essential and instructive roles in regulating gene expression, bioRxiv (Preprint Server) 2019, (2019)

Huber, W.; von Heydebreck, A.; Vingron, M.: Error models for microarray intensities, (2004)