Author

Xinyang Feng

Columbia UniversityORCID 0000-0003-0331-0488Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:imaging· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:benchmarking· 1 Approach:statistical model· 1

Frequent Co-Authors

Frank A. ProvenzanoiD· 2 shared Scott A. SmalliD· 2 shared Adam T. EggebrechtiD· 1 shared Alzheimer’s Disease Neuroimaging Initiative· 1 shared Andy Eck· 1 shared Ari Segel· 1 shared Babatunde Adeyemo· 1 shared J. Jenny LiiD· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersNeuroscience

Frank A. ProvenzanoORCID

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3 papers

Recent·Most cited

Network level enrichment provides a framework for biological interpretation of machine learning results

J. Jenny Li, Ari Segel, Xinyang Feng, Jiaxin Cindy Tu, Andy Eck, Kelsey T. King, Babatunde Adeyemo, Nicole R. Karcher, Likai Chen, Adam T. Eggebrecht, Muriah D. Wheelock

This study applies machine learning to neuroimaging data to identify brain connectivity biomarkers linked to age, emphasizing the importance of accounting for family variance and using network enrichment for biological interpretation.

Network Neuroscience · Jan 1, 2024Peer-reviewedNeuroscience

Detecting prodromal Alzheimer’s disease with MRI through deep learning

Xinyang Feng, Frank A. Provenzano, Scott A. Small, Alzheimer’s Disease Neuroimaging Initiative

This paper develops a deep learning model using MRI to classify Alzheimer’s disease and finds it outperforms traditional biomarkers in detecting prodromal disease and predicting progression.

bioRxiv · Oct 22, 2019 · PreprintNeuroscience

Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification

Xinyang Feng, Jie Yang, Zachary C. Lipton, Scott A. Small, Frank A. Provenzano

This study applies 3D CNNs to MRI data to classify Alzheimer’s disease, showing high accuracy and identifying the hippocampal formation as a key predictive region.

bioRxiv · Oct 31, 2018 · PreprintNeuroscience