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Pengyu Hong

Researcher at Brandeis University

Publications -  102
Citations -  2871

Pengyu Hong is an academic researcher from Brandeis University. The author has contributed to research in topics: Computer science & Gesture. The author has an hindex of 26, co-authored 92 publications receiving 2557 citations. Previous affiliations of Pengyu Hong include Stanford University & Association for Computing Machinery.

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Evidence of off-target effects associated with long dsRNAs in Drosophila melanogaster cell-based assays

TL;DR: This analysis predicts that dsRNAs containing ≥19-nucleotide perfect matches identified in silico to unintended targets may contribute to a significant false positive error rate arising from off-target effects and suggests simple guidelines to help ensure high-quality information from RNAi high-throughput screens.
Proceedings ArticleDOI

Gesture modeling and recognition using finite state machines

TL;DR: A state-based approach to gesture learning and recognition is proposed, using spatial clustering and temporal alignment to build a finite state machine (FSM) recognizer.
Proceedings ArticleDOI

Incorporate support vector machines to content-based image retrieval with relevance feedback

TL;DR: This paper proposes an approach to utilize both positive and negative feedbacks for image retrieval that releases the user from manually providing preference weight for each positive example.
Journal ArticleDOI

The Hippo Signaling Pathway Interactome

TL;DR: A proteomics approach for protein-protein interactions reveals new components of a conserved cell signaling pathway, and selects for further characterization a new member of the alpha-arrestin family, Leash, and shows that it promotes degradation of Yki through the lysosomal pathway.
Journal ArticleDOI

Comparative Analysis of Argonaute-Dependent Small RNA Pathways in Drosophila

TL;DR: A comparative and comprehensive RNAi screen is undertaken to identify genes that impact three major Ago-dependent small RNA pathways that operate in Drosophila S2 cells and identifies subsets of candidates that act positively or negatively in siRNA, endo-siRNA, and miRNA pathways.