Sequence2Vec specifications

Information


Unique identifier OMICS_20076
Name Sequence2Vec
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input data A DNA sequence.
Operating system Unix/Linux
Programming languages C++
Computer skills Advanced
Stability Stable
Maintained Yes

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Versioning


No version available

Maintainers


  • person_outline Xin Gao
  • person_outline Le Song

Publication for Sequence2Vec

Sequence2Vec citation

library_books

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape

2017
Bioinformatics
PMCID: 5870668
PMID: 28961686
DOI: 10.1093/bioinformatics/btx480

[…] ding sequences as a hidden Markov model (HMM). However, instead of performing standard maximum likelihood estimation for these models, we devised a new message passing-like embedding approach, called Sequence2Vec, which maps these HMMs into a common nonlinear feature space and uses these embedded features to build a nonlinear predictive model. Importantly, unlike many existing methods that conside […]

Sequence2Vec institution(s)
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA; King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
Sequence2Vec funding source(s)
Supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/1976-04 and URF/1/3007-01; by NSF IIS-1218749, NIH BIGDATA 1R01GM108341, NSF CAREER IIS-1350983, NSF IIS- 1639792 EAGER, ONR N00014-15-1-2340, NVIDIA, Intel and Amazon AWS.

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