Author

Y. Linda Hu

University of RegensburgORCID 0009-0002-7263-3635Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:whole-exome sequencing· 3 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:trajectory inference· 1

Frequent Co-Authors

Andreas LöschiD· 3 shared Rainer SpangiD· 3 shared Rudolf SchilliD· 3 shared Kevin RuppiD· 2 shared Lars GrasedyckiD· 2 shared Maren KleveriD· 2 shared Niko BeerenwinkeliD· 2 shared Simon PfahleriD· 2 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Andreas LöschORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A Scalable Framework for Pan-Cancer Tumor Evolution Analysis Enables Transfer of Progression Mechanisms Across Tumor Entities

Simon Pfahler, Andreas Lösch, Y. Linda Hu, Rudolf Schill, Rainer Spang, Tilo Wettig B

fastMHN enables pan-cancer tumor evolution analysis by approximating complex progression models, identifying a progression group linked to STK11 mutations across multiple cancer types.

bioRxiv · Jan 21, 2026 · PreprintBioinformatics

Modeling metastatic progression from cross-sectional cancer genomics data

Kevin Rupp, Andreas Lösch, Y. Linda Hu, Chenxi Nie, Rudolf Schill, Maren Klever, Simon Pfahler, Lars Grasedyck, Tilo Wettig B, Niko Beerenwinkel, Rainer Spang

This study introduces metMHN, a model to infer metastatic progression from cross-sectional cancer genomics data, identifying TP53 and EGFR as key mediators and highlighting copy number alterations in post-seeding adaptation.

bioRxiv · Jan 30, 2024 · PreprintBioinformatics

Overcoming Observation Bias for Cancer Progression Modeling

Rudolf Schill, Maren Klever, Andreas Lösch, Y. Linda Hu, Stefan Vocht, Kevin Rupp, Lars Grasedyck, Rainer Spang, Niko Beerenwinkel

This paper extends cancer progression models to account for observation bias by modeling how genetic events affect tumor detection, improving the accuracy of inferred causal interactions.

bioRxiv · Dec 3, 2023 · PreprintBioinformatics