Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R  Toolbox (AdaptGauss)

Ultsch, Alfred; Thrun, Michael C.; Hansen-Goos, Onno; Lötsch, Jörn

doi:10.3390/ijms161025897

Cited by 47 publications

(53 citation statements)

References 48 publications

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“…Of note, when using visually guided EM based GMM fitting, results were similar to those obtained with "Distribution Optimization". Main advantages of the latter algorithm, however, are that it excludes the subjective (pain thresholds to heat stimuli acquired in healthy volunteers 12 ). The distribution of the data is shown as probability density function (PDF) estimated by means of the Pareto density estimation (PDE 23 ; black line) and overlaid on a histogram.…”

Section: Discussionmentioning

confidence: 99%

Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures

Lerch

Ultsch

Lötsch

2020

Sci Rep

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finding subgroups in biomedical data is a key task in biomedical research and precision medicine. Already one-dimensional data, such as many different readouts from cell experiments, preclinical or human laboratory experiments or clinical signs, often reveal a more complex distribution than a single mode. Gaussian mixtures play an important role in the multimodal distribution of one-dimensional data. However, although fitting of Gaussian mixture models (GMM) is often aimed at obtaining the separate modes composing the mixture, current technical implementations, often using the Expectation Maximization (EM) algorithm, are not optimized for this task. This occasionally results in poorly separated modes that are unsuitable for determining a distinguishable group structure in the data. Here, we introduce "Distribution Optimization" an evolutionary algorithm to GMM fitting that uses an adjustable error function that is based on chi-square statistics and the probability density. the algorithm can be directly targeted at the separation of the modes of the mixture by employing additional criterion for the degree by which single modes overlap. The obtained GMM fits were comparable with those obtained with classical EM based fits, except for data sets where the EM algorithm produced unsatisfactory results with overlapping Gaussian modes. there, the proposed algorithm successfully separated the modes, providing a basis for meaningful group separation while fitting the data satisfactorily. Through its optimization toward mode separation, the evolutionary algorithm proofed particularly suitable basis for group separation in multimodally distributed data, outperforming alternative EM based methods.

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Section: Discussionmentioning

confidence: 99%

Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures

Lerch

Ultsch

Lötsch

2020

Sci Rep

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“…Source: The visualisation was generated using the R package 'AdaptGauss' available on CRAN (Ultsch, Thrun, Hansen-Goos, & Lötsch, 2015).…”

Section: Figure 1a Gmm (Red) Is Based On the Pdf Of Dtw Distances (Bmentioning

confidence: 99%

Cluster Analysis of Per Capita Gross Domestic Products

Thrun¹

2019

EBER

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A B S T R A C T Objective:The purpose of this article is to show the value of exploratory data analysis performed on the multivariate time series dataset of gross domestic products per capita (GDP) of 160 countries for the years 1970-2010. New knowledge can be derived by applying cluster analysis to the time series of GDP to show how patterns in GDP can be explained in a data-driven way. Research Design & Methods:Patterns characterised by distance and density based structures were found in a topographic map by using dynamic time warping distances with the Databionic swarm (DBS) 1 . The topographic map represents a 3D landscape of data structures. Looking at the topographic map, the number of clusters was derived. Then, a DBS clustering was performed and the quality of the clustering was verified. Findings: Two clusters are identified in the topographic map. The rules deduced from classification and regression tree (CART) show that the clusters are defined by an event occurring in 2001 at which time the world economy was experiencing its first synchronised global recession in a quarter-century. Geographically, the first cluster mostly of African and Asian countries and the second cluster consists mostly of European and American countries. Implications & Recommendations: DBS can be used even by non-professionals in the field of data mining and knowledge discovery. DBS is the first swarm-based clustering technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organisation, and game theory.

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“…This is a kernel density estimator that has been developed with the focus to be particularly suitable for the discovery of groups in the data . An implementation can be found in the R package “AdaptGauss” (https://cran.r-project.org/package=AdaptGauss). In the present example, the PDE (Figure bottom) clearly showed a dominance of zero values in “Lab3” and emphasized the rather broad variance suggesting accidental measurements in the rest of this parameter.…”

Section: Descriptive Statistical Analysis Of the Three Laboratory Parmentioning

confidence: 99%

Data visualizations to detect systematic errors in laboratory assay results

Lötsch

2017

Pharmacology Res & Perspec

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The measurement of concentrations of drugs and endogenous substances is widely used in basic and clinical pharmacology research and service tasks. Using data science‐derived visualizations of laboratory data, it is demonstrated on a real‐life example that basic statistical exploration of laboratory assay results or advised standard visual methods of data inspection may fall short in detecting systematic laboratory errors. For example, data pathologies such as generating always the same value in all probes of a particular assay run may pass undetected when using standard methods of data quality check. It is shown that the use of different data visualizations that emphasize different views of the data may enhance the detection of systematic laboratory errors. A dotplot of single data in the order of assay is proposed that provides an overview on the data range, outliers and a particular type of systematic errors where similar values are wrongly measured in all probes.

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Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)

Cited by 47 publications

References 48 publications

Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures

Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures

Cluster Analysis of Per Capita Gross Domestic Products

Data visualizations to detect systematic errors in laboratory assay results

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