Author

D. Mehrotra

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 Animal Behavior & Cognition· 1 Bioengineering· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Approach:deep learning· 2 Organism:human· 2 Assay:behavioral assay· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Organism:mouse· 1

Frequent Co-Authors

A. Geraldes· 2 shared C. Bass· 2 shared J. Hense· 2 shared J. Schmidt· 2 shared P. Pandya· 2 shared P. Raharja-Liu· 2 shared S. Arslan· 2 shared S. Singhal· 2 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersCancer Biology

A. Geraldes

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Author profile →

4 papers

Recent·Most cited

Label-free 3-D molecular imaging of living tissues using Raman Spectral Projection Tomography

E. Stepula, A. R. Walther, D. Mehrotra, M. Jensen, M. H. Yuan, S. V. Pedersen, E. Gentleman, M. B. Albro, M. A. B. Hedegaard, M. S. Bergholt

This paper introduces Raman spectral projection tomography (RSPT) for label-free 3-D molecular imaging of tissues with sub-millimeter resolution and demonstrates its application in imaging molecular gradients and extracellular matrix heterogeneities.

bioRxiv · Sep 23, 2023 · PreprintBioengineering

Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale study

S. Arslan, D. Mehrotra, J. Schmidt, A. Geraldes, S. Singhal, J. Hense, X. Li, C. Bass, J. N. Kather, P. Pandya, P. Raharja-Liu

This study shows deep learning can predict multi-omic biomarkers from H&E-stained cancer slides, with 50% of models achieving AUC > 0.633 across 32 cancer types.

bioRxiv · Jan 21, 2022 · PreprintCancer Biology

Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learning

S. Arslan, X. Li, J. Schmidt, J. Hense, A. Geraldes, C. Bass, K. Brown, A. Marcia, T. Dewhirst, P. Pandya, S. Singhal, D. Mehrotra, P. Raharja-Liu

The paper validates a deep learning method that predicts breast cancer biomarkers from H&E slides with high accuracy in a clinical setting.

bioRxiv · Jan 4, 2022 · PreprintCancer Biology

Novel measures of Morris water maze performance that use vector field maps to assess accuracy, uncertainty, and intention of navigational searches

M. Prabod Kumar, D. Mehrotra, N. Nruythyathi, D. Almeida-Filho, Y.-S. Lee, A. J. Silva, B. Jayaprakash

This paper introduces novel vector field-based measures to assess spatial memory in the Morris water maze, revealing previously undetected differences in mouse models of Noonan syndrome.

bioRxiv · Jul 4, 2021Animal Behavior & Cognition