Author

Chenxiao Xu

Stony Brook University

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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Neuroscience· 2

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Hyoung Seop KimiD· 2 shared Ji Hwan ParkiD· 2 shared Jiook ChaiD· 2 shared Jong Hun KimiD· 2 shared Seonjoo LeeiD· 2 shared Shinjae YooiD· 2 shared Yaakov SterniD· 2 shared Yun WangiD· 2 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 36 mutual co-author links

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2 shared papersNeuroscience

Hyoung Seop KimORCID

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2 papers

Recent·Most cited

Diagnosis and Prognosis of Alzheimer’s Disease Using Brain Morphometry and White Matter Connectomes

Yun Wang, Chenxiao Xu, Ji Hwan Park, Seonjoo Lee, Yaakov Stern, Shinjae Yoo, Jong Hun Kim, Hyoung Seop Kim, Jiook Cha, The Alzheimer’s Disease Neuroimaging Initiative

This paper uses multimodal MRI and machine learning to classify Alzheimer's disease and mild cognitive impairment, showing high accuracy in diagnosis and progression prediction.

NeuroImage Clinical · Jan 1, 2019Peer-reviewedNeuroscience

Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes

Yun Wang, Chenxiao Xu, Ji Hwan Park, Seonjoo Lee, Yaakov Stern, Shinjae Yoo, Jong Hun Kim, Hyoung Seop Kim, Jiook Cha

This paper uses machine learning on brain morphometry and connectomes from MRI to predict Alzheimer's disease risk and progression, achieving high accuracy in both single-site and multi-site datasets.

bioRxiv · Sep 4, 2018 · PreprintNeuroscience