Tag: Clustering

DRAGON – Matlab package of DRAGON Clustering approach

DRAGON

:: DESCRIPTION

DRAGON (Divisive hierarchical maximum likelihood clustering) was verified on mutation and microarray data, and was gauged against standard clustering methods in the literature. Its validation included synthetic and significant biological data.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Matlab

:: DOWNLOAD

DRAGON

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2017 Dec 28;18(Suppl 16):546. doi: 10.1186/s12859-017-1965-5.
Divisive hierarchical maximum likelihood clustering.
Sharma A, López Y, Tsunoda T.

SIML – Matlab package of SIML Clustering approach

SIML

:: DESCRIPTION

The proposed SIML (Stepwise Iterative Maximum Likelihood) clustering algorithm has been tested on microarray datasets and SNP datasets. It has been compared with a number of clustering algorithms.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Matlab

:: DOWNLOAD

SIML

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2016 Aug 24;17(1):319. doi: 10.1186/s12859-016-1184-5.
Stepwise iterative maximum likelihood clustering approach.
Sharma A, Shigemizu D, Boroevich KA, López Y, Kamatani Y, Kubo M, Tsunoda T

2D-EM – Matlab package of 2D-EM Clustering approach

2D-EM

:: DESCRIPTION

2D-EM is a clustering algorithm approach designed for small sample size and high-dimensional datasets. To employ information corresponding to data distribution and facilitate visualization, the sample is folded into its two-dimension (2D) matrix form (or feature matrix). The maximum likelihood estimate is then estimated using a modified expectation-maximization (EM) algorithm.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Matlab

:: DOWNLOAD

2D-EM

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2017 Dec 28;18(Suppl 16):547. doi: 10.1186/s12859-017-1970-8.
2D-EM clustering approach for high-dimensional data through folding feature vectors.
Sharma A, Kamola PJ, Tsunoda T.

HML – Tool to perform Hierarchical Maximum Likelihood (HML) Clustering

HML

:: DESCRIPTION

HML is an algorithm which uses distribution and centroid information to cluster a sample and was applied to biological data.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

  HML

:: MORE INFORMATION

Citation

IEEE Trans Biomed Eng. 2016 Mar 24.
Hierarchical Maximum Likelihood Clustering Approach.
Sharma A, Boroevich K, Shigemizu D, Kamatani Y, Kubo M, Tsunoda T.

gCluster – General Clustering Method

gCluster

:: DESCRIPTION

The gCluster algorithm is a general clustering method that predicts clusters of any biological word or combination of them, relying only on the DNA sequence and the statistical significance. When using CG as word, gCluster works similarly to CpGcluster, our method to predict CpG islands. More broadly, gCluster has much in common with wordCluster but uses an improved distance model.

::DEVELOPER

The group of computational genomics and bioinformatics at Granada University (Spain)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • VirtualBox.

:: DOWNLOAD

gCluster

:: MORE INFORMATION

Citation

Gómez-Martín C., Lebrón R., Oliver J.L., Hackenberg M.
Prediction of CpG Islands as an Intrinsic Clustering Property Found in Many Eukaryotic DNA Sequences and Its Relation to DNA Methylation.
Methods Mol Biol. 2018;1766:31-47. doi: 10.1007/978-1-4939-7768-0_3.

APCluster 1.4.8 – Affinity Propagation Clustering

APCluster 1.4.8

:: DESCRIPTION

The apcluster package implements Frey’s and Dueck’s Affinity Propagation clustering in R. The algorithms are largely analogous to the Matlab code published by Frey and Dueck. The package further provides an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 APCluster for R

:: MORE INFORMATION

Citation

U. Bodenhofer, A. Kothmeier, and S. Hochreiter (2011).
APCluster: an R package for affinity propagation clustering.
Bioinformatics 27:2463-2464

clusterScore 0.12 – Clustering of Cavbase Scores and other proximity matrices

clusterScore 0.12

:: DESCRIPTION

clusterScore is a software to explore the important parameters of a clustering procedure, which will allow an accurate classification of proteins. It has been successfully applied on two diverse and challenging data sets. The predicted number of clusters, as suggested by clusterScore and the subsequent clustering of proteins are in agreement with the EC and Merops classifications. Furthermore, putative cross-reactivity mapped between calpain-1 and cysteine cathepsins on structural level has so far only been described based on ligand data.

::DEVELOPER

Group of Prof. Dr. G. Klebe

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

clusterScore 

:: MORE INFORMATION

Citation

J Chem Inf Model. 2013 Aug 26;53(8):2082-92. doi: 10.1021/ci300550a.
Cavities tell more than sequences: exploring functional relationships of proteases via binding pockets.
Glinca S, Klebe G.

kClust – Fast and Sensitive Clustering of large Protein Sequence Databases

kClust

:: DESCRIPTION

kClust is a fast and sensitive clustering method for the clustering of protein sequences. It is able to cluster large protein databases down to 20-30% sequence identity. kClust generates a clustering where each cluster is represented by its longest sequence (representative sequence).

::DEVELOPER

Söding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

kClust

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013 Aug 15;14:248. doi: 10.1186/1471-2105-14-248.
kClust: fast and sensitive clustering of large protein sequence databases.
Hauser M, Mayer CE, Söding J.

GraphCrunch 2.1.1 – Network Modeling, Alignment and Clustering

GraphCrunch 2.1.1

:: DESCRIPTION

GraphCrunch is a  software tool for network analysis, modeling and alignment. It automates tasks of finding the best fitting model for the network data, pairwise comparisons of networks, alignment of two networks using GRAAL algorithm (a better network alignment algorithm, MI-GRAAL, is also available for download), and provides capabilities of clustering network nodes based on their topological surrounding in the network.

::DEVELOPER

Nataša Pržulj, Imperial College

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • QT

:: DOWNLOAD

 GraphCrunch

:: MORE INFORMATION

Citation

GraphCrunch 2: Software tool for network modeling, alignment and clustering.
Kuchaiev O, Stevanović A, Hayes W, Pržulj N.
BMC Bioinformatics. 2011 Jan 19;12:24. doi: 10.1186/1471-2105-12-24.

LRAcluster 1.0 – Low Rank Approximation based Multi-omics Data Clustering

LRAcluster 1.0

:: DESCRIPTION

LRAcluster is a new method to discover molecular subtypes by detecting the low-dimensional intrinsic space of high-dimensional cancer multi-omics data.

::DEVELOPER

Bioinformatics & Intelligent Information Processing Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

 LRAcluster

:: MORE INFORMATION

Citation

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
Wu D, Wang D, Zhang MQ, Gu J.
BMC Genomics. 2015 Dec 1;16(1):1022. doi: 10.1186/s12864-015-2223-8.

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