Author

L. C. Hermida

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

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Cancer Biology· 4

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Research Focus:gene regulatory network inference· 2 Assay:whole-genome sequencing· 2 Research Focus:clustering· 1

Frequent Co-Authors

E. Ruppin· 3 shared J. Zhang· 2 shared A. G. Sowalsky· 1 shared A. Lebensohn· 1 shared A. Mungall· 1 shared A. Rajan· 1 shared A. S. Gamis· 1 shared B. Morrow· 1 shared

Activity by Year

2017-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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3 shared papersCancer Biology

E. Ruppin

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Author profile →

4 papers

Recent·Most cited

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

D.-T. Hoang, G. Dinstag, L. C. Hermida, D. S. Ben-Zvi, E. Elis, K. Caley, S.-J. Sammut, S. Sinha, N. Sinha, C. H. Dampier, C. Stossel, T. Patil, A. Rajan, W. Lassoued, J. Strauss, , E. Ruppin

ENLIGHT-DeepPT predicts cancer treatment response from H&E slides by first imputing transcriptomics and then using that data to predict response across multiple therapies and cancer types.

bioRxiv · Jun 7, 2022 · PreprintCancer Biology

Transcriptomics based prediction of survival and response to therapy in malignant mesothelioma

N. U. Nair, Q. Jiang, J. S. Wei, V. A. Misra, B. Morrow, C. Kesserwan, L. C. Hermida, J. S. Lee, I. Mian, J. Zhang, A. Lebensohn, M. Sengupta, J. Khan, E. Ruppin, R. Hassan

The paper identifies a 48-gene prognostic signature and synthetic-lethality profiles predictive of survival and therapy response in mesothelioma using transcriptomic and exomic data from 122 patients.

bioRxiv · Apr 13, 2022 · PreprintCancer Biology

Predicting cancer prognosis and drug response from the tumor microbiome

L. C. Hermida, E. M. Gertz, E. Ruppin

This study shows that tumor microbial abundances can predict cancer prognosis and drug response, though less effectively than gene expression data.

bioRxiv · Jul 21, 2020Cancer Biology

Comprehensive characterization of pediatric AML reveals diverse fusion oncoproteins and age-specific mutational interactions

H. Bolouri, J. E. Farrar, T. J. Triche, R. E. Ries, E. L. Lim, T. A. Alonzo, Y. Ma, R. Moore, A. Mungall, M. A. Marra, J. Zhang, X. Ma, Y. Liu, Y. Liu, J. M. Guidry Auvil, , S. Meshinchi

This study characterizes the molecular landscape of pediatric AML, identifying age-specific mutations and fusion oncoproteins that differ from adult AML.

bioRxiv · Oct 10, 2017Cancer Biology