Author

Sayed Hadi Hashemi

University of Illinois Urbana-Champaign

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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Genomics· 2 Neuroscience· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 5 Research Focus:differential expression· 4 Approach:machine learning (classical)· 3 Approach:experimental assay· 2 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1

Frequent Co-Authors

Andrew SingletoniD· 5 shared Faraz FaghriiD· 5 shared Mike A. NallsiD· 5 shared Hampton L. LeonardiD· 3 shared Roy H. CampbelliD· 3 shared Anant DaduiD· 2 shared Cornelis BlauwendraatiD· 2 shared Hirotaka IwakiiD· 2 shared

Activity by Year

2018-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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Curved chords = mutual co-authorship

5 shared papersGenomics

Andrew SingletonORCID

Mutual co-authors in cluster

Author profile →

5 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

Multi-Modality Machine Learning Predicting Parkinsons Disease

Mary B. Makarious, Hampton L. Leonard, Dan Vitale, Hirotaka Iwaki, Lana J. Sargent, Anant Dadu, Ivo Violich, Elizabeth D. Hutchins, David Saffo, Sara Bandrés‐Ciga, Jonggeol Jeff Kim, Yeajin Song, Melina Maleknia, Matt Bookman, Willy Nojopranoto, , Mike A. Nalls

This paper develops an automated ML model to predict Parkinson's Disease using multi-modal data, achieving high AUC scores and constructing gene networks relevant to PD.

npj Parkinson s Disease · Apr 1, 2022Peer-reviewedGenomics

A CRISPRi/a platform in iPSC-derived microglia uncovers regulators of disease states

Nina M. Dräger, Sydney M. Sattler, Cindy Tzu-Ling Huang, Olivia M. Teter, Kun Leng, Sayed Hadi Hashemi, Jason Y. Hong, Giovanni Aviles, Claire Dudley Clelland, Lihong Zhan, Joe C. Udeochu, Lay Kodama, Andrew Singleton, Mike A. Nalls, Justin K. Ichida, , Martin Kampmann

This study uses a CRISPRi/a platform in iPSC-derived microglia to identify genes regulating microglia states and their therapeutic targets.

bioRxiv · Jun 16, 2021 · PreprintNeuroscience

Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis

Ruilin Tian, Anthony Abarientos, Jason Y. Hong, Sayed Hadi Hashemi, Rui Yan, Nina M. Dräger, Kun Leng, Mike A. Nalls, Andrew Singleton, Ke Xu, Faraz Faghri, Martin Kampmann

The paper performs genome-wide CRISPRi/a screens in human neurons to identify gene functions related to oxidative stress and neurodegenerative diseases, finding that lysosomal failure links to ferroptosis.

Nature Neuroscience · May 24, 2021Peer-reviewedGenomics

Predicting onset, progression, and clinical subtypes of Parkinson disease using machine learning

Faraz Faghri, Sayed Hadi Hashemi, Hampton L. Leonard, Sonja W. Scholz, Roy H. Campbell, Mike A. Nalls, Andrew Singleton

This study uses machine learning to identify Parkinson disease subtypes and predict progression, finding three distinct subtypes with accurate progression predictions.

bioRxiv · Jun 5, 2018 · PreprintNeuroscience