Understanding and improving high-throughput sequencing data production and analysis - Martin Kircher (2011)

freeIbis: an efficient basecaller with calibrated quality scores for Illumina sequencers. - Gabriel Renaud; Martin Kircher; Udo Stenzel; Janet Kelso (Bioinform., 2013)

CADD: predicting the deleteriousness of variants throughout the human genome. - Philipp Rentzsch; Daniela M. Witten; Gregory M. Cooper; Jay Shendure; Martin Kircher (Nucleic Acids Res., 2019)

Bayesian Optimization Improves Tissue-Specific Prediction of Active Regulatory Regions with Deep Neural Networks. - Luca Cappelletti; Alessandro Petrini; Jessica Gliozzo; Elena Casiraghi; Max Schubach; Martin Kircher; Giorgio Valentini (IWBBIO, 2020)

HemoMIPs - Automated analysis and result reporting pipeline for targeted sequencing data. - Philip Kleinert; Beth K. Martin; Martin Kircher (PLoS Comput. Biol., 2020)

Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques. - Luca Cappelletti; Alessandro Petrini; Jessica Gliozzo; Elena Casiraghi; Max Schubach; Martin Kircher; Giorgio Valentini (BMC Bioinform., 2022)

Predicting the pathogenicity of missense variants using features derived from AlphaFold2. - Axel Schmidt 007; Sebastian Röner; Karola Mai; Hannah Klinkhammer; Martin Kircher; Kerstin U. Ludwig (Bioinform., 2023)

Benchmarking machine learning methods for the identification of mislabeled data. - Lusine Nazaretyan; Ulf Leser; Martin Kircher (Artif. Intell. Rev., 2025)

Using individual barcodes to increase quantification power of massively parallel reporter assays. - Pia Keukeleire; Jonathan D. Rosen; Angelina Göbel-Knapp; Kilian Salomon; Max Schubach; Martin Kircher (BMC Bioinform., 2025)