Author

Tim Conrad

Zuse Institute BerlinORCID 0000-0002-5590-5726Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Assay:proteomics· 2 Approach:statistical model· 2 Assay:CUT&Tag· 1

Frequent Co-Authors

Åslaug HellandiD· 1 shared Astrid WeißiD· 1 shared Borong ShaoiD· 1 shared Christof SchütteiD· 1 shared Harm Jan BogaardiD· 1 shared Janina Krentz· 1 shared Katharina BaumiD· 1 shared Maria Moksnes BjaanæsiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Åslaug HellandORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJul 16, 2026

Differential multi-omics analysis of pulmonary arterial hypertension microvascular endothelial cells for differential drug response

Pauline Hiort, Astrid Weiß, Janina Krentz…·Katharina Baum

The paper uses a multi-omics framework to analyze PAH endothelial cells and identifies kinase-related alterations and potential drug targets.

DOI 10.64898/2026.07.10.737730

humanproteomics

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BioinformaticsIEEE Transactions on Computational Biology and BioinformaticsPeer-reviewedJan 10, 2022

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

Sahar Iravani, Tim Conrad

This paper introduces DLearnMS, a deep learning framework for biomarker detection in LC-MS proteomics data, showing improved performance with fewer false positives.

9 citations

proteomicsdeep learning

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BioinformaticsPLoS ONEPeer-reviewedJan 31, 2019

EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma

Borong Shao, Maria Moksnes Bjaanæs, Åslaug Helland…·Tim Conrad

The paper introduces a network-based feature selection framework for lung adenocarcinoma prognosis, achieving high prediction performance using EMT-related signatures.

7 citations

humanbulk RNA-seq

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