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Sergey M. Plis
Researcher at Georgia Institute of Technology
Publications - 177
Citations - 7167
Sergey M. Plis is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 27, co-authored 150 publications receiving 5588 citations. Previous affiliations of Sergey M. Plis include MIND Institute & University of New Mexico.
Papers
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Journal ArticleDOI
Tracking Whole-Brain Connectivity Dynamics in the Resting State
Elena A. Allen,Eswar Damaraju,Sergey M. Plis,Erik B. Erhardt,Tom Eichele,Vince D. Calhoun,Vince D. Calhoun +6 more
TL;DR: In this article, the authors describe an approach to assess whole-brain functional connectivity dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices.
Journal ArticleDOI
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
Journal ArticleDOI
Deep learning for neuroimaging: A validation study
Sergey M. Plis,R Devon Hjelm,Ruslan Salakhutdinov,Elena A. Allen,Elena A. Allen,HJ Bockholt,Jeffrey D. Long,Jeffrey D. Long,Hans J. Johnson,Hans J. Johnson,Jane S. Paulsen,Jane S. Paulsen,Jessica A. Turner,Vince D. Calhoun,Vince D. Calhoun +14 more
TL;DR: In this article, a constraint-based approach to visualizing high dimensional data was proposed to analyze the effect of parameter choices on data transformations and showed that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
Journal ArticleDOI
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Alexander Aliper,Sergey M. Plis,Artem V. Artemov,Alvaro Ulloa,Polina Mamoshina,Alex Zhavoronkov,Alex Zhavoronkov +6 more
TL;DR: This work demonstrates a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions and proposes using deep neural net confusion matrices for drug repositioning.
Proceedings Article
Deep learning for neuroimaging: A validation study
TL;DR: In this article, a constraint-based approach to visualizing high dimensional data is proposed to analyze the effect of parameter choices on data transformations and show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.