HEA-PSP – Ab-initio Protein Structure Prediction

HEA-PSP

:: DESCRIPTION

HEA-PSP is a hybrid evolutionary search framework with various crossover implementations for Ab-initio protein structre prediction

::DEVELOPER

Computational Biology lab, George Mason University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  HEA-PSP

:: MORE INFORMATION

Citation

Brian Olson, Kenneth A. De Jong, and Amarda Shehu.
Off-Lattice Protein Structure Prediction with Homologous Crossover.
GECCO 2013, pages 287-294, Amsterdam,

IPred 201409 – Integrate ab initio and evidence based Gene Predictions

IPred 201409

:: DESCRIPTION

IPred (Integrate gene Predictions) is a program that combines the output of ab initio and evidence based (including comparative based) gene finders to improve on the overall prediction accuracy.

::DEVELOPER

IPred team

: SCREENSHOTS

IPred

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Python
  • Java

:: DOWNLOAD

 IPred

:: MORE INFORMATION

Citation

BMC Genomics. 2015 Feb 26;16:134. doi: 10.1186/s12864-015-1315-9.
IPred – integrating ab initio and evidence based gene predictions to improve prediction accuracy.
Zickmann F, Renard BY

LncADeep – ab initio lncRNA Identification and Functional Annotation tool

LncADeep

:: DESCRIPTION

LncADeep is an ab initio lncRNA identification and functional annotation tool based on deep learning.First, LncADeep identifies lncRNAs by integrating sequence intrinsic and homology features based on deep belief networks. Second, LncADeep predicts lncRNA-protein interactions using sequence and structure features based on deep neural networks. Third, since accurate lncRNA-protein interactions can help to infer the functions of lncRNAs.

::DEVELOPER

ZhuLab, Peking Uiniversity, Beijing

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WebBrowser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Bioinformatics. 2018 Nov 15;34(22):3825-3834. doi: 10.1093/bioinformatics/bty428.
LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning.
Yang C, Yang L, Zhou M, Xie H, Zhang C, Wang MD, Zhu H.

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