Author

N. Madhukar

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 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

O. Elemento· 3 shared C. Gilvary· 2 shared A. Perez· 1 shared C. Henchcliffe· 1 shared C. S. Leslie· 1 shared D. Sackett· 1 shared E. V. Navarro· 1 shared G. Galletti· 1 shared

Activity by Year

2019-2019 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersCancer Biology

O. Elemento

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Imatinib overrides taxane resistance by selective inhibition of novel CLIP1 variant obstructing the microtubule pore

K. Kita, P. V. Thakkar, G. Galletti, N. Madhukar, E. V. Navarro, I. Barasoain, H. V. Goodson, D. Sackett, J. F. Diaz, O. Elemento, M. A. Shah, P. Giannakakou

The paper identifies a novel CLIP-170 variant, CLIP-170S, that causes taxane resistance in gastric cancer and shows imatinib can reverse this resistance by selectively inhibiting CLIP-170S.

bioRxiv · Nov 11, 2019 · PreprintCancer Biology

A machine learning and network framework to discover new indications for small molecules

C. Gilvary, J. Elkhader, N. Madhukar, C. Henchcliffe, M. Goncalves, O. Elemento

This paper introduces a machine learning framework, CATNIP, to predict novel drug indications using molecular features, identifying potential therapies for diseases like Parkinson's and Type 2 Diabetes.

bioRxiv · Aug 28, 2019Pharmacology & Toxicology

A machine learning approach predicts essential genes and pharmacological targets in cancer

C. Gilvary, N. Madhukar, K. Gayvert, M. Foronda, A. Perez, C. S. Leslie, L. Dow, G. Pandey, O. Elemento

This paper introduces ECLIPSE, a machine learning model that predicts essential genes and pharmacological targets in cancer by integrating genomic, cell line, and experimental features, showing better differentiation of approved cancer drugs from non-approved ones.

bioRxiv · Jul 4, 2019 · PreprintCancer Biology