Author

Ayse B. Dincer

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· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:deep learning· 3 Assay:bulk RNA-seq· 2 Research Focus:dimensionality reduction· 2 Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Joseph D. JanizekiD· 2 shared Su‐In LeeiD· 2 shared Devin K. SchweppeiD· 1 shared Kamila NaxerovaiD· 1 shared Mikaël J. PittetiD· 1 shared Safiye ÇelikiD· 1 shared Sewoong OhiD· 1 shared Wei QiuiD· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Joseph D. JanizekORCID

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Author profile →

3 papers

Recent·Most cited

A deep profile of gene expression across 18 human cancers

Wei Qiu, Ayse B. Dincer, Joseph D. Janizek, Safiye Çelik, Mikaël J. Pittet, Kamila Naxerova, Su‐In Lee

The paper introduces DeepProfile, a deep learning framework for analyzing gene expression in 18 cancers, revealing biological insights through latent space analysis and linking gene expression to clinical outcomes.

bioRxiv · Mar 17, 2024 · PreprintBioinformatics

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

Ayse B. Dincer, Yang Lu, Devin K. Schweppe, Sewoong Oh, William Stafford Noble

This paper introduces a deep learning model to reduce sequence-specific biases in quantitative mass spectrometry data, improving correlation with mRNA expression.

Journal of Proteome Research · Jun 13, 2022Peer-reviewedBioinformatics

Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

Ayse B. Dincer, Joseph D. Janizek, Su‐In Lee

The paper introduces AD-AE, an adversarial deconfounding autoencoder that generates gene expression embeddings free of confounders and capable of generalizing across domains.

Bioinformatics · Sep 11, 2020Peer-reviewedBioinformatics