Author

Adam Linder

Lund UniversityORCID 0000-0002-8187-7239Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:proteomics· 3 Organism:human· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Christofer A. Q. KarlssoniD· 3 shared Johan A. MalmströmiD· 3 shared Lars MalmströmiD· 3 shared Tirthankar MohantyiD· 3 shared Aaron M. ScottiD· 2 shared Erik MalmströmiD· 2 shared Alexander Doyle· 1 shared Andreas ForsvalliD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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3 shared papersSystems Biology

Christofer A. Q. KarlssonORCID

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3 papers

Recent·Most cited

Inferring the composition of the blood plasma proteome by a human proteome distribution atlas

Erik Malmström, Lars Malmström, Simon Hauri, Tirthankar Mohanty, Aaron M. Scott, Christofer A. Q. Karlsson, Carlos Alberto Gueto-Tettay, Emma Åhrman, Shahab Nozohoor, Bobby Tingstedt, Sara Regnér, Peter Elfving, Leif Hilding Bjermer, Andreas Forsvall, Alexander Doyle, , Johan A. Malmström

The paper develops a proteomics strategy to infer the origin of proteins in human plasma using a proteome atlas and RNA/protein atlases, demonstrating its application in disease cohorts.

Cell · Apr 8, 2025Peer-reviewedSystems Biology

A pharmacoproteomic landscape of organotypic intervention responses in gram-negative sepsis

Tirthankar Mohanty, Christofer A. Q. Karlsson, Yashuan Chao, Erik Malmström, Eleni Bratanis, Andrietta Grentzmann, Martina Mørch, Victor Nizet, Lars Malmström, Adam Linder, Oonagh Shannon, Johan A. Malmström

This study uses pharmacoproteomics to analyze treatment responses in a murine sepsis model, identifying distinct response patterns and drug interactions across organs and time.

Nature Communications · Jun 17, 2023Peer-reviewedSystems Biology

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

Aaron M. Scott, Christofer A. Q. Karlsson, Tirthankar Mohanty, Suvi T. Vaara, Adam Linder, Johan A. Malmström, Lars Malmström

This paper introduces GPS, a machine learning-based method for improving peptide and protein identification accuracy and rates in DIA proteomics by using an external dataset, and demonstrates its effectiveness in identifying biomarkers for acute kidney injury.

bioRxiv · Nov 3, 2022 · PreprintBioinformatics