Author

Rachneet Kaur

University of Illinois Urbana-ChampaignORCID 0000-0002-9223-4546Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Research Focus:differential expression· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1

Frequent Co-Authors

Anant DaduiD· 3 shared Andrew SingletoniD· 3 shared Faraz FaghriiD· 3 shared Hirotaka IwakiiD· 3 shared Mike A. NallsiD· 3 shared Roy H. CampbelliD· 3 shared Vipul K. SatoneiD· 3 shared Ali DaneshmandiD· 2 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Anant DaduORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

Anant Dadu, Vipul K. Satone, Rachneet Kaur, Sayed Hadi Hashemi, Hampton L. Leonard, Hirotaka Iwaki, Mary B. Makarious, Kimberley J. Billingsley, Sara Bandrés‐Ciga, Lana J. Sargent, Alastair John Noyce, Ali Daneshmand, Cornelis Blauwendraat, KENNETH L. MAREK, Sonja W. Scholz, , Faraz Faghri

This study uses machine learning to identify Parkinson's disease subtypes and predict progression in two cohorts, finding three subtypes with distinct progression rates and high prediction accuracy.

npj Parkinson s Disease · Dec 16, 2022Peer-reviewedBioinformatics

Application of Aligned-UMAP to longitudinal biomedical studies

Anant Dadu, Vipul K. Satone, Rachneet Kaur, Mathew J. Koretsky, Hirotaka Iwaki, Yue Andy Qi, Daniel M. Ramos, Brian Avants, Jacob Y. Hesterman, Roger N. Gunn, Mark Cookson, Michael Emmerson Ward, Andrew Singleton, Roy H. Campbell, Mike A. Nalls, Faraz Faghri

This paper applies Aligned-UMAP to longitudinal biomedical datasets to reveal time-dependent patterns and provides best practices for its use.

Patterns · Jan 1, 2022Peer-reviewedBioinformatics

Predicting Alzheimer's disease progression trajectory and clinical subtypes using machine learning

Vipul K. Satone, Rachneet Kaur, Anant Dadu, Hampton L. Leonard, Hirotaka Iwaki, Mary B. Makarious, Lana J. Sargent, Alzheimer’s Disease Neuroimaging Initiative, Ali Daneshmand, Sonja W. Scholz, Andrew Singleton, Mike A. Nalls, Roy H. Campbell, Faraz Faghri

This paper uses machine learning to predict Alzheimer's disease progression rates and classify clinical subtypes based on early clinical data.

bioRxiv · Aug 26, 2020 · PreprintNeuroscience