Author

Safiye Celik

University of Washington

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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Bioinformatics· 1 Cancer Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Approach:statistical model· 2 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Su-In Lee· 2 shared Ayse Berceste Dincer· 1 shared Benjamin A Logsdon· 1 shared Charles W Drescher· 1 shared David Hawkins· 1 shared Joseph D. Janizek· 1 shared Mara Rendi· 1 shared Naozumi HiranumaiD· 1 shared

Activity by Year

2016-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersCancer Biology

Su-In Lee

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Author profile →

3 papers

Recent·Most cited

Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine

Joseph D. Janizek, Safiye Celik, Su-In Lee

This study introduces TreeCombo, an XGBoost model predicting synergistic drug combinations using drug properties and gene expression, outperforming DeepSynergy and providing interpretable results.

bioRxiv · May 27, 2018 · PreprintCancer Biology

DeepProfile: Deep learning of cancer molecular profiles for precision medicine

Ayse Berceste Dincer, Safiye Celik, Naozumi Hiranuma, Su‐In Lee

DeepProfile trains a variational autoencoder on thousands of unlabeled gene expression samples to learn low-dimensional representations that improve prediction of acute myeloid leukemia drug responses and cancer phenotypes.

bioRxiv · May 26, 2018 · PreprintBioinformatics

Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

Safiye Celik, Benjamin A Logsdon, Stephanie Battle, Charles W Drescher, Mara Rendi, David Hawkins, Su-In Lee

The paper introduces INSPIRE, a method to infer gene modules and their dependencies across multiple datasets, identifying HOPX as a potential driver of tumor-associated stroma in ovarian cancer.

bioRxiv · Apr 13, 2016Systems Biology