Author

M. Lehman

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2 Plant Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Rockstroh· 2 shared C. C. Nelson· 2 shared A. Anand· 1 shared A. Gupta· 1 shared A. Jain· 1 shared A. Kahmen· 1 shared A. Majumdar· 1 shared B. G. Hollier· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersCancer Biology

A. Rockstroh

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Author profile →

3 papers

Recent·Most cited

Valine Catabolism Drives Bioenergetic and Lipogenic Fuel Plasticity in Prostate Cancer

C. L. Bidgood, L. K. Philp, A. Rockstroh, M. Lehman, C. C. Nelson, M. C. Sadowski, J. H. Gunter

This study identifies valine catabolism as a key driver of bioenergetic and lipogenic flexibility in prostate cancer, with potential therapeutic implications.

bioRxiv · Jan 1, 2024 · PreprintCancer Biology

Unsaturated intercellular vapor pressure is relevant for leaf water heavy isotope enrichment

C. Angove, M. Lehman, M. Saurer, Y. Tang, P. Kilpeläinen, A. Kahmen, P. P. Schiestl-Aalto, O.-P. Tikkasalo, J. K. Bäck, K. T. Rinne-Garmston

This study quantifies how unsaturated intercellular vapor pressure affects leaf water heavy isotope enrichment using boreal forest and large-scale data, showing its relevance via Mechanism 1.

bioRxiv · Sep 12, 2023 · PreprintPlant Biology

Gene expression based inference of drug resistance in cancer

S. Chawla, A. Rockstroh, M. Lehman, E. Rather, A. Jain, A. Anand, A. Gupta, N. Bhattacharya, S. Poonia, P. Rai, N. Das, A. Majumdar, J. Jayadeva, G. Ahuja, B. G. Hollier, , D. Sengupta

This study uses gene expression data and machine learning to predict drug resistance in cancer by analyzing pathway activity patterns rather than gene expression levels alone.

bioRxiv · Nov 17, 2021Cancer Biology