Holger Moch

Decoding the Plasma Proteomic Landscape of Clear Cell Renal Cell Carcinoma Reveals Diagnostic and Prognostic Liquid Biopsy Biomarkers

This paper identifies plasma proteomic biomarkers for diagnosing and prognosticating clear cell renal cell carcinoma through longitudinal mass spectrometry profiling.

Compressed sensing expands the multiplexity of imaging mass cytometry

The paper extends compressed sensing to imaging mass cytometry, enabling higher multiplexity by recovering spatial protein expression from fewer measured channels.

RIPPLET: Mutation-Only Gene and Pathway Profiling for Precision Oncology

RIPPLET converts somatic DNA variants into gene-impact scores and pathway-perturbation profiles, outperforming multi-omic tools in identifying cancer drivers and predicting drug response without RNA data.

DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images

DeepSpot predicts spatial transcriptomics from H&E images, improving gene correlations across cancer datasets and generating virtual spatial transcriptomics samples.

Deep visual multi-omics profiling reveals mechanisms that underlie cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

This study uses AI-based image analysis and multi-omics profiling to link morphological features with molecular changes in ccRCC, identifying unique molecular profiles in aggressive rhabdoid tumors.

Heterogeneous and Novel Transcript Expression in Single Cells of Patient-Derived ccRCC Organoids

This study uses single-cell long-read sequencing to analyze transcriptomes of ccRCC organoids, identifying novel transcripts and highlighting cell-specific alternative splicing events.

Comparison of Single-cell Long-read and Short-read Transcriptome Sequencing of Patient-derived Organoid Cells of ccRCC: Quality Evaluation of the MAS-ISO-seq Approach

Comparison of calling pipelines for whole genome sequencing: an empirical study demonstrating the importance of mapping and alignment

This study compares WGS pipelines, finding DRAGEN outperforms GATK in speed and accuracy for variant calling and mapping.

scROSHI - robust supervised hierarchical identification of single cells

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

CanIsoNet: A Database to Study the Functional Impact of Isoform Switching Events in Diseases

CanIsoNet is a database that integrates isoform expression data with interaction networks and ClinVar annotations to predict the pathogenic impact of isoform switching events in diseases.

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.