ProtDec-LTR protocols

View ProtDec-LTR computational protocol

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ProtDec-LTR specifications

Information


Unique identifier OMICS_19364
Name ProtDec-LTR
Interface Web user interface
Restrictions to use None
Input data Some protein sequences.
Input format FASTA
Computer skills Basic
Version 2.0
Maintained Yes

Documentation


Maintainer


  • person_outline Bin Liu <>

Additional information


http://bioinformatics.hitsz.edu.cn/ProtDec-LTR2.0/tutorial/

Publications for ProtDec-LTR

ProtDec-LTR in pipeline

2018
PMCID: 5773712
PMID: 29348418
DOI: 10.1038/s41598-018-19491-y

[…] classifiers (ann, random forest) have been widely used in the field of bioinformatics, and some predictors have been established based on these classifiers, such as psfm-dbt, 2l-pirna, pse-analysis, protdec-ltr, protdec-ltr2.0, etc. some powerful protein analysis methods have been proposed for the formulation of biological sequences, such as pse-in-one, repdna, based on different functions […]


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ProtDec-LTR in publications

 (2)
PMCID: 5773712
PMID: 29348418
DOI: 10.1038/s41598-018-19491-y

[…] classifiers (ann, random forest) have been widely used in the field of bioinformatics, and some predictors have been established based on these classifiers, such as psfm-dbt, 2l-pirna, pse-analysis, protdec-ltr, protdec-ltr2.0, etc. some powerful protein analysis methods have been proposed for the formulation of biological sequences, such as pse-in-one, repdna, based on different functions […]

PMCID: 4972358
PMID: 27487242
DOI: 10.1371/journal.pone.0157994

[…] based on the rank-learning framework, in which visual features and click features are simultaneously utilized to obtain the ranking model. liu b et al. [] propose a new computational method called protdec-ltr for protein remote homology detection, which is able to combine various ranking methods in a supervised manner via using the rank-learning algorithm. the results indicate predictive […]


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ProtDec-LTR institution(s)
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
ProtDec-LTR funding source(s)
Supported by the National Natural Science Foundation of China (No. 61672184), the Natural Science Foundation of Guangdong Province (2014A030313695), Guangdong Natural Science Funds for Distinguished Young Scholars (2016A030306008), and Scientific Research Foundation in Shenzhen (Grant No. JCYJ20150626110425228).

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