Browse
Submit Data
Databases
API
Help

Dataset Information

30 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Unsupervised ranking of clustering algorithms by INFOMAX.

ABSTRACT: Clustering and community detection provide a concise way of extracting meaningful information from large datasets. An ever growing plethora of data clustering and community detection algorithms have been proposed. In this paper, we address the question of ranking the performance of clustering algorithms for a given dataset. We show that, for hard clustering and community detection, Linsker's Infomax principle can be used to rank clustering algorithms. In brief, the algorithm that yields the highest value of the entropy of the partition, for a given number of clusters, is the best one. We show indeed, on a wide range of datasets of various sizes and topological structures, that the ranking provided by the entropy of the partition over a variety of partitioning algorithms is strongly correlated with the overlap with a ground truth partition The codes related to the project are available in https://github.com/Sandipan99/Ranking_cluster_algorithms.

SUBMITTER: Sikdar S

PROVIDER: S-EPMC7588117 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Unsupervised ranking of clustering algorithms by INFOMAX.

Sikdar Sandipan S Mukherjee Animesh A Marsili Matteo M

PloS one 20201026 10

Clustering and community detection provide a concise way of extracting meaningful information from large datasets. An ever growing plethora of data clustering and community detection algorithms have been proposed. In this paper, we address the question of ranking the performance of clustering algorithms for a given dataset. We show that, for hard clustering and community detection, Linsker's Infomax principle can be used to rank clustering algorithms. In brief, the algorithm that yields the high ...[more]

PMID: 33104709

Similar Datasets

Unsupervised clustering and epigenetic classification of single cells

Project description:96 scATAC-seq samples generated for the retinoic acid-induced mESC differentiation at day 4.

2018-04-20 | GSE107651 | GEO

AGRA: analysis of gene ranking algorithms.

Project description:UnlabelledOften, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it.Availability and implementationAvailable at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server.Contactsimon.kocbek@uni-mb.si.

| S-EPMC3072556 | biostudies-literature

An unsupervised neuromorphic clustering algorithm.

Project description:Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need "neuromorphic algorithms" that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module.

| S-EPMC6658584 | biostudies-literature

Clustering algorithms: A comparative approach.

Project description:Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.

| S-EPMC6333366 | biostudies-literature

Unsupervised statistical clustering of environmental shotgun sequences.

Project description:BACKGROUND: The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed. RESULTS: We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer distributions. The formalism is implemented using a Markov Chain Monte Carlo approach in a k-mer feature space. We introduce a space transformation that reduces the dimensionality of the feature space and a genomic fragment divergence measure that strongly correlates with the method's performance. Pairwise analysis of over 1000 completely sequenced genomes reveals that the vast majority of genomes have sufficient genomic fragment divergence to be amenable for binning using the present formalism. Using a high-performance implementation, the binner is able to classify fragments as short as 400 nt with accuracy over 90% in simulations of low-complexity communities of 2 to 10 species, given sufficient genomic fragment divergence. The method is available as an open source package called LikelyBin. CONCLUSION: An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity samples. For medium and high complexity samples, we discuss the possibility of combining the current method with other methods as part of an iterative process to enhance the resolving power of sorting reads into taxonomic and/or functional bins.

| S-EPMC2765972 | biostudies-literature

Unsupervised hierarchical clustering of iHPCs induced by 9 or 10 TFs

2011-06-07 | GSE29730 | GEO

Unsupervised hierarchical clustering of iNPCs induced by 6 or 5 TFs

2011-06-07 | GSE29724 | GEO

Unsupervised hierarchical clustering of iHPCs induced by 9 or 10 TFs

Project description:To clarify the gene expression profile of iHep, microarray analysis was performed using iHeps induced by 10 TFs (Foxg1, Lcor, Hnf3b, Hnf4a, Foxo6, Cdx2, Tcf1, Foxa3 ,Tcf2, Onecut1) and 9 TFs (Onecut1 was omitted from 10 TFs). Unsupervised hierarchical clustering indicated that iHep is expressing a global transcriptional profile more similar to that of HPCs rather than that of NPCs, and suggested that TFs present in the pool acted as inducing TFs. HPC (HB1 and HNG2) were established from fetal liver (E13.5) of C57BL6J and STOCK Tg(Nanog-GFP, Puro)1 Yam, respectively. iHeps were induced from NPC (NSBAg2, established from an ES cell line BAg73C2 carrying beta-geo knock-in allele in Afp) using retroviral vectors (pMXs without drug-selection markers) of 9 or 10 transcription factors. Three weeks after the infection, G418 was added and cultured for 1 week before the harvest. NSBAg2 and NSEB5-2C were used for the data of NPC. GSM396240 and GSM336010 were used for the data of ESC.

2011-06-07 | E-GEOD-29730 | biostudies-arrayexpress

Unsupervised hierarchical clustering of iNPCs induced by 6 or 5 TFs

Project description:To clarify the gene expression profile of iNPC, microarray analysis was performed using iNPCs induced by 6 TFs (Pax6, Hmga2, Etv6, Gatad2b, Nfxl1, and Esx1) and 5 TFs (Esx1 was omitted from 6 TFs). Unsupervised hierarchical clustering indicated that iNPC is expressing a global transcriptional profile more similar to that of NPCs rather than that of MEFs, and suggested that the TFs present in the pool acted as inducing TFs. iNPCs were induced from MEF using 6 or 5 transcription factors. iNPCs were induced from MEF (MEFSH, derived from mice carrying IRES-Hygro in Sox allele) using retroviral vectors (pMXs-IRESNeo) of 6 or 5 transcription factors. Four weeks after the infection, Hygromycin was added and cultured for 1 week before the harvest. NSBAg2 and NSEB5-2C were used for the data of NPC. GSM396240 and GSM336010 were used for the data of ESC.GSM651349 and GSM336011 were used for the data of MEF.

2011-06-07 | E-GEOD-29724 | biostudies-arrayexpress

Predicting disease risk using bootstrap ranking and classification algorithms.

Project description:Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

| S-EPMC3749941 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data