Hendrik Weisser

This study uses machine learning to predict genes associated with RNA methylation pathways in humans using multi-omics data from the Harmonizome database.

This study uses machine learning to predict novel therapeutic targets in nine cancers by analyzing mutation, gene expression, and essentiality data.

The paper introduces NASE, a database search engine for RNA mass spectrometry data, demonstrating its ability to identify modified RNA sequences in three datasets.

The paper evaluates a DIWA method to decompress isobaric labelling ratios by using narrow isolation widths for quantification and standard widths for identification, improving accuracy while maintaining coverage.