Author

Vojo P. Deretic

University of New MexicoORCID 0000-0002-3624-5208Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cell Biology· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Brett S. PhinneyiD· 1 shared Daniel R. Byrd· 1 shared Einar S TrosdaliD· 1 shared Emily HendrixiD· 1 shared Fulong WangiD· 1 shared Jason Porter WeickiD· 1 shared Jeremy J. YangiD· 1 shared Jessica BinderiD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperCell Biology

Brett S. PhinneyORCID

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3 papers

Recent·Most cited

Cell BiologyeLifePeer-reviewedSep 2, 2024

Noncanonical roles of ATG5 and membrane atg8ylation in retromer assembly and function

Masroor Ahmad Paddar, Fulong Wang, Einar S Trosdal…·Vojo P. Deretic

This study shows ATG5 and membrane atg8ylation regulate retromer function in GLUT1 trafficking, independent of canonical autophagy.

11 citations

experimental assaycell signaling & trafficking

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NeurosciencePLoS ONEPeer-reviewedMar 24, 2020

Optical induction of autophagy via Transcription factor EB (TFEB) reduces pathological tau in neurons

Jessica Binder, Praveen N. Chander, Vojo P. Deretic…·Kiran Bhaskar

This study uses optogenetic control of TFEB to reduce pathological tau in human iPSC-neurons derived from AD patients.

19 citations

humanexperimental assay

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BioinformaticsbioRxivPreprintJul 26, 2019

Autophagy dark genes: Can we find them with machine learning?

Tudor Ionel Oprea, Jeremy J. Yang, Daniel R. Byrd, Vojo P. Deretic

The paper uses machine learning to identify potential autophagy-related genes, finding 23% of top predictions in existing databases and suggesting 193 genes as possible 'dark genes'.

4 citations

humanmachine learning (classical)

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