AdipoCount – Automatic Adipocyte Counting

AdipoCount

:: DESCRIPTION

AdipoCount is an automatic adipoytes counting system using image processing algorithms, which can significantly imporve the efficency of adipoytes counting.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

AdipoCount

:: MORE INFORMATION

Citation:

Front Physiol. 2018 Feb 20;9:85. doi: 10.3389/fphys.2018.00085. eCollection 2018.
AdipoCount: A New Software for Automatic Adipocyte Counting
Xuhao Zhi, Jiqiu Wang, Peng Lu, Jue Jia, Hong-Bin Shen, Guang Ning

AnnoFly – Annotation of Drosophila Embryonic Images Based on an Attention-Enhanced RNN Model

AnnoFly

:: DESCRIPTION

AnnoFly is a new annotator for the fruit fly embryonic images.Driven by an attention-enhanced RNN model, it can weight images of different qualities, so as to focus on the most informative image patterns.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

AnnoFly 

:: MORE INFORMATION

Citation:

Bioinformatics. 2019 Aug 15;35(16):2834-2842. doi: 10.1093/bioinformatics/bty1064.
AnnoFly: Annotating Drosophila Embryonic Images Based on an Attention-Enhanced RNN Model
Yang Yang , Mingyu Zhou , Qingwei Fang , Hong-Bin Shen

PseAAC / PseAAC-Builder 3.0 / PseAAC-General – Generating Pseudo Amino Acid Composition

PseAAC / PseAAC-Builder 3.0 / PseAAC-General

:: DESCRIPTION

PseAAC is an algorithm that could convert a protein sequence into a digital vector that could be processed by pattern recognition algorithms. The design of PseAAC incorporated the sequence order information to improve the conventional amino acid compositions. The application of pseudo amino acid composition is very common, including almost every branch of computational proteomics.

PseAAC-Builder (PseAAC-General) is a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions.

::DEVELOPER

PseAAC team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows with Cygwin

:: DOWNLOAD

 PseAAC-Builder 

:: MORE INFORMATION

Citation:

Hong-Bin Shen and Kuo-Chen Chou.
PseAAC: a flexible web-server for generating various kinds of protein pseudo amino acid composition.
Analytical Biochemistry, 2008, 373: 386-388.

Anal Biochem. 2012 Jun 15;425(2):117-9. doi: 10.1016/j.ab.2012.03.015. Epub 2012 Mar 27.
PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions.
Du P1, Wang X, Xu C, Gao Y.

Pufeng Du, Shuwang Gu, Yasen Jiao.
PseAAC-General: Fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets.
International Journal of Molecular Sciences 15 (2014) pp.3495-3506

EzyPred – Predicting Enzyme Functional Classes and Sub-classes

EzyPred

:: DESCRIPTION

EzyPred is a top-down approach for predicting enzyme functional classes and sub-classes purely based on protein sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

EzyPred: a top-down approach for predicting enzyme functional classes and subclasses.
Shen HB, Chou KC.
Biochem Biophys Res Commun. 2007 Dec 7;364(1):53-9.

COMSPA – Predicting Protein Structural Classes from Primary Sequence

COMSPA

:: DESCRIPTION

COMSPA is a web server for predicting protein structural classes from primary sequence by learning multi-view features in complex space

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Amino Acids. 2013 May;44(5):1365-79. doi: 10.1007/s00726-013-1472-6. Epub 2013 Feb 28.
Learning protein multi-view features in complex space.
Yu DJ1, Hu J, Wu XW, Shen HB, Chen J, Tang ZM, Yang J, Yang JY.

Cell-PLoc 2.0 – Predicting Subcellular localization of Proteins in different Organisms

Cell-PLoc 2.0

:: DESCRIPTION

Cell-PLoc is a package of Web servers for predicting subcellular localization of proteins in various organisms.The package contains the following six predictors: Euk-mPLoc 2.0 , Hum-mPLoc 2.0, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively.

::DEVELOPER

Computational Systems Biology GroupShanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Nat Protoc. 2008;3(2):153-62. doi: 10.1038/nprot.2007.494.
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.
Chou KC1, Shen HB.

Kuo-Chen Chou, and Hong-Bin Shen,
Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms,
Natural Science, 2010, 2: 1090-1103
PLoS One. 2010 Apr 1;5(4):e9931. doi: 10.1371/journal.pone.0009931.
A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0.
Chou KC1, Shen HB.

A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0.
Shen HB, Chou KC.
Anal Biochem. 2009 Nov 15;394(2):269-74. doi: 10.1016/j.ab.2009.07.046.

Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization.
Chou KC, Shen HB.
PLoS One. 2010 Jun 28;5(6):e11335. doi: 10.1371/journal.pone.0011335.

Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins.
Shen HB, Chou KC.
Protein Pept Lett. 2009;16(12):1478-84.

Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins.
Shen HB, Chou KC.
J Theor Biol. 2010 May 21;264(2):326-33. doi: 10.1016/j.jtbi.2010.01.018.

J Biomol Struct Dyn. 2010 Oct;28(2):175-86.
Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites.
Shen HB1, Chou KC.

HIVcleave – Predicting HIV Protease Cleavage Sites in Proteins

HIVcleave

:: DESCRIPTION

HIVcleave is a web-server for predicting HIV protease cleavage sites in proteins

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Anal Biochem. 2008 Apr 15;375(2):388-90. doi: 10.1016/j.ab.2008.01.012. Epub 2008 Jan 15.
HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins.
Shen HB1, Chou KC.

LabCaS – Prediction of the Calpain Substrate Cleavage Sites

LabCaS

:: DESCRIPTION

LabCaS  (Labeling Calpain substrate cleavage Sites) is a new computational approach for accurate prediction of the calpain substrate cleavage sites from amino acid sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Proteins. 2013 Apr;81(4):622-34. doi: 10.1002/prot.24217. Epub 2012 Dec 24.
LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields.
Fan YX1, Zhang Y, Shen HB.

LnSignal – Predicting Protein N-terminal Signal Peptides

LnSignal

:: DESCRIPTION

The web server LnSignal (Labelling N-terminal Signal petide cleavage site) was developed by integrating position-specific amino acid propensities based on the highest average positions and conditional random fields.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Yong-Xian Fan, Jiangning Song, Chen Xu and Hong-Bin Shen,
Predicting protein N-terminal signal peptides using position-specific amino acid propensities and conditional random fields ,
Current Bioinformatics, 2013, 8: 183-192.

LR_PPI – large-scale Prediction of Human protein-protein interaction

LR_PPI

:: DESCRIPTION

LR_PPI is a web server for large-scale prediction of human protein-protein interaction from amino acid sequence based on latent topic feature

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

J Proteome Res. 2010 Oct 1;9(10):4992-5001. doi: 10.1021/pr100618t.
Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features.
Pan XY1, Zhang YN, Shen HB.