GLaD – A Mixed-Membership Model for Heterogenous Tumor Subtype Classification

GLaD

:: DESCRIPTION

GLAD is a mixed-membership classification model that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample.

::DEVELOPER

Flaherty Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • Python

:: DOWNLOAD

  GLaD

:: MORE INFORMATION

Citation

GLAD: A mixed-membership model for heterogeneous tumor subtype classification.
Saddiki H, McAuliffe J, Flaherty P.
Bioinformatics. 2014 Sep 29. pii: btu618.

MetaBinG 0.4 / MetaBinG2 – ultra-fast Metagenomic Sequence Classification system using GPUs

MetaBinG 0.4 / MetaBinG2

:: DESCRIPTION

MetaBinG  is an ultra-fast metagenomic sequence classification system using graphic processing units (GPUs).

MetaBinG2 is a fast and accurate metagenomic sequence classification system for samples with many unknown organisms

::DEVELOPER

Dr. Chaochun Wei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MetaBinG / MetaBinG2

:: MORE INFORMATION

Citation:

Biol Direct. 2018 Aug 22;13(1):15. doi: 10.1186/s13062-018-0220-y.
MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.
Qiao Y, Jia B, Hu Z, Sun C, Xiang Y, Wei C.

PLoS One. 2011;6(11):e25353. doi: 10.1371/journal.pone.0025353. Epub 2011 Nov 23.
MetaBinG: using GPUs to accelerate metagenomic sequence classification.
Jia P1, Xuan L, Liu L, Wei C.

PlantMiRNAPred – Classification of Real and Pseudo Plant Pre-miRNAs

PlantMiRNAPred

:: DESCRIPTION

PlantMiRNAPred web server can be used to classify real plant pre-miRNAs and pseudo hairpins.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 May 15;27(10):1368-76. doi: 10.1093/bioinformatics/btr153. Epub 2011 Mar 26.
PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.
Xuan P, Guo M, Liu X, Huang Y, Li W, Huang Y.

CancerIN – Classification and Designing of Anticancer Compounds

CancerIN

:: DESCRIPTION

CancerIN  is a web server developed for predicting anticancer activity of molecules. Similarity based approach has been used for discrimination or classification of anticancer and non-anticancer molecule.

::DEVELOPER

CancerIN team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOs
  • Python

:: DOWNLOAD

CancerIN

:: MORE INFORMATION

Citation

Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.
Singh H, Kumar R, Singh S, Chaudhary K, Gautam A, Raghava GP.
BMC Cancer. 2016 Feb 9;16:77. doi: 10.1186/s12885-016-2082-y.

PROCLASS – Protein Structure Classification Server

PROCLASS

:: DESCRIPTION

 PROCLASS allows to predict the class of protein from its amino acid sequence. It predict weather protein belong to class Alpha or Beta or Alpha+Beta or Alpha/Beta.

::DEVELOPER

Dr. G P S Raghava,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Raghava, G P S (1999)
Proclass: A computer program for predicting the protein structural classes.
J. Biosciences 24, 176.

GPCRSclass – SVM based Classification of Amine Type of GPCR

GPCRSclass

:: DESCRIPTION

 GPCRSclass is a dipeptide composition based method for predicting Amine Type of G-protein-coupled receptors. In this method type amine is predicted from dipeptide composition of proteins using SVM.

::DEVELOPER

 GPCRSclass Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M, Raghava GPS.
GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors.
Nucleic Acids Res.(2005). 33(Web Server issue):W143-7

RNAcon 1.0 – Classification of non-coding RNAs (ncRNA)

RNAcon 1.0

:: DESCRIPTION

RNAcon is a web-server for the prediction and classification of non-coding RNAs. It uses SVM-based model for the discrimination between coding and ncRNAs and RandomForest-based prediction model for the classification of ncRNAs into different classes. The structural information based graph properties were used for the development of prediction model.

::DEVELOPER

RNAcon team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RNAcon

:: MORE INFORMATION

Citation

Panwar, B; Arora, A. and Raghava GP (2014)
Prediction and classification of ncRNAs using structural information
BMC Genomics 2014, 15:127

EGFRpred – Classification of Active and Inactive Anti-egfr Compounds

EGFRpred

:: DESCRIPTION

EGFRpred is a web service for the predicting and designing of inhibitors against EGFR (a cell surface receptor whose overexpression is known to cause cancer).

::DEVELOPER

EGFRpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.
Singh H, Singh S, Singla D, Agarwal SM, Raghava GP.
Biol Direct. 2015 Mar 25;10(1):10.

GECES – Gene Expression Classification

GECES

:: DESCRIPTION

GECES is a web-server for Gene expression classification using epigenetic features and DNA sequence composition in the human embryonic stem cell line H1

::DEVELOPER

The Li’s Group of Theoretical Biophysics and Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Gene. 2016 Oct 30;592(1):227-234. doi: 10.1016/j.gene.2016.07.059. .
Gene expression classification using epigenetic features and DNA sequence composition in the human embryonic stem cell line H1.
Su WX, Li QZ, Zhang LQ, Fan GL, Wu CY, Yan ZH, Zuo YC

 

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Clean_tree 2 – High-resolution Male Lineage Classification

Clean_tree 2

:: DESCRIPTION

Clean tree is a tool intended to help interpret phylogenetic SNP genotypes obtained from NGS-data, furthermore it uses a quality control system to ensure the accuracy of the obtained results.

::DEVELOPER

Dept Genetic Identification ErasmusMC Rotterdam

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • R

:: DOWNLOAD

  Clean tree

:: MORE INFORMATION

Citation

Ralf, A., van Oven, M., Zhong, K., Kayser, M.
Simultaneous analysis of hundreds of Y-chromosomal SNPs for high-resolution paternal lineage classification using targeted semiconductor sequencing.
Hum Mutat. 2015 36: 151-9. PubMed: 25338970

Forensic Y-SNP analysis beyond SNaPshot: High-resolution Y-chromosomal haplogrouping from low quality and quantity DNA using Ion AmpliSeq and targeted massively parallel sequencing.
Ralf A, Oven M, Montiel González D, de Knijff P, van der Beek K, Wootton S, Lagacé R, Kayser M.
Forensic Science International: Genetics , 2019