Ngoc Hieu Tran

Orcid: 0000-0001-5186-8852

According to our database1, Ngoc Hieu Tran authored at least 15 papers between 2013 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2022
A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.
Briefings Bioinform., 2022

2021
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices.
Nat. Mach. Intell., 2021

2020
Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines.
Nat. Mach. Intell., 2020

PointIso: Point Cloud Based Deep Learning Model for Detecting Arbitrary-Precision Peptide Features in LC-MS Map through Attention Based Segmentation.
CoRR, 2020

2019
DeepNovoV2: Better de novo peptide sequencing with deep learning.
CoRR, 2019

2018
Hybrid privacy-preserving clinical decision support system in fog-cloud computing.
Future Gener. Comput. Syst., 2018

DeepIso: A Deep Learning Model for Peptide Feature Detection.
CoRR, 2018

2017
De novo peptide sequencing by deep learning.
Proc. Natl. Acad. Sci. USA, 2017

CCA Secure encryption supporting authorized equality test on ciphertexts in standard model and its applications.
Inf. Sci., 2017

Protein identification with deep learning: from abc to xyz.
CoRR, 2017

2016
AMAS: Optimizing the Partition and Filtration of Adaptive Seeds to Speed up Read Mapping.
IEEE ACM Trans. Comput. Biol. Bioinform., 2016

Privacy-Preserving and Verifiable Data Aggregation.
Proceedings of the Singapore Cyber-Security Conference (SG-CRC) 2016, 2016

Efficient Verifiable Computation of Linear and Quadratic Functions over Encrypted Data.
Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016

2015
AMAS: optimizing the partition and filtration of adaptive seeds to speed up read mapping.
CoRR, 2015

2013
Counting Motifs in the Entire Biological Network from Noisy and Incomplete Data - (Extended Abstract).
Proceedings of the Research in Computational Molecular Biology, 2013


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