Author

B. Haibe-Kains

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· 7 Cell Biology· 1

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Organism:human· 7 Assay:bulk RNA-seq· 5 Research Focus:drug response / drug discovery· 4 Approach:statistical model· 4 Research Focus:differential expression· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Approach:experimental assay· 2

Frequent Co-Authors

P. Smirnov· 3 shared A. S. Mer· 2 shared L. Penn· 2 shared S. K. Nair· 2 shared T. J. Pugh· 2 shared W. Ba-alawi· 2 shared Z. Safikhani· 2 shared A. Dhabaria· 1 shared

Activity by Year

2017-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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3 shared papersCancer Biology

P. Smirnov

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Author profile →

8 papers

Recent·Most cited

GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data

E. So, S. Hayat, S. Kadambat Nair, B. Wang, B. Haibe-Kains

GraphComm predicts cell-cell communication in single-cell RNAseq data by integrating gene expression with protein interaction networks and spatial information.

bioRxiv · Apr 26, 2023 · PreprintCell Biology

Meta-analysis of preclinical pharmacogenomic studies to discover robust and translatable biomarkers of drug response

P. Smirnov, S. K. Nair, F. Abbas-Aghababazadeh, N. Feizi, I. Smith, T. J. Pugh, B. Haibe-Kains

This study identifies 4,338 statistically significant biomarkers of drug response across 8 tissue types and 34 drugs using meta-analysis of preclinical pharmacogenomic studies, with ODC1 showing clinical relevance for Paclitaxel response in breast cancer.

bioRxiv · Oct 22, 2022 · PreprintCancer Biology

Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

W. Ba-alawi, S. K. Nair, B. Li, A. Mammoliti, P. Smirnov, A. S. Mer, L. Penn, B. Haibe-Kains

The paper develops an interpretable machine learning pipeline to predict drug sensitivity in cancer cells using bimodal gene expression patterns from pharmacogenomic data.

bioRxiv · Sep 8, 2020 · PreprintCancer Biology

Computational pharmacogenomics screen identifies synergistic statin-compound combinations as anti-breast cancer therapies

J. E. van Leeuwen, W. Ba-Alawi, E. Branchard, J. Longo, J. Silvester, D. W. Cescon, B. Haibe-Kains, L. Penn, D. Gendoo

bioRxiv · Sep 7, 2020 · PreprintCancer Biology

Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer

C. A. Chang, J. Jen, S. Jiang, A. Sayad, A. S. Mer, K. R. Brown, A. Nixon, A. Dhabaria, K. H. Tang, D. Venet, C. Sotiriou, J. Deng, K.-k. Wong, S. Adams, P. Meyn, , B. G. Neel

This study identifies drug-tolerant persisters (DTPs) in HER2+ breast cancer and reveals their origins and vulnerabilities to TKI resistance.

bioRxiv · Aug 28, 2020Cancer Biology

Single-cell chromatin accessibility in glioblastoma delineates cancer stem cell heterogeneity predictive of survival

P. Guilhamon, M. M. Kushida, A. Nikolic, D. Singhal, G. MacLeod, S. A. M. Tonekaboni, F. M. Cavalli, C. Arlidge, N. Rajakulendran, N. Rastegar, X. Hao, R. Hassam, L. J. Smith, H. Whetstone, F. J. Coutinho, , M. Lupien

The paper uses single-cell chromatin accessibility to identify distinct glioblastoma stem cell states predictive of patient survival.

bioRxiv · Mar 13, 2020 · PreprintCancer Biology

Karyotypic divergence confounds cellular phenotypes in large pharmacogenomic studies

R. Quevedo, N. El-Hachem, P. Smirnov, Z. Safikhani, T. J. Pugh, B. Haibe-Kains

This study identifies discordances in genotypic and karyotypic profiles across pharmacogenomic datasets, highlighting the impact of copy-number variants on drug response and the need for verification of cell line profiles.

bioRxiv · Mar 11, 2019 · PreprintCancer Biology

Consensus on Molecular Subtypes of Ovarian Cancer

G. M. Chen, L. Kannan, L. Geistlinger, V. Kofia, Z. Safikhani, D. M. A. Gendoo, G. Parmigiani, M. Birrer, B. Haibe-Kains, L. Waldron

The paper evaluates three ovarian cancer subtype classifiers, finds they are not robust, and proposes a consensus classifier with a 100-gene signature that improves stratification of a minority of tumors.

bioRxiv · Jul 12, 2017Cancer Biology