A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

Steed, Chad A.; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T. J.

doi:10.2172/1035521

Cited by 3 publications

(4 citation statements)

References 30 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We explored class interrelationship using a multivariate data visualization tool called CrossVis. CrossVis represents an evolution of both the EDEN and MDX visual analysis systems 41,42 (see Methods section for details). For clarity, the expected Gaussian value µ was removed since its values are identical to the mean area of pores (Fig.…”

Section: Automatic Recognition Of Relevant Features and Extraction Ofmentioning

confidence: 99%

“…The CrossVis system is a visual analytics system, which combines interactive data visualization and statistical analytics techniques. The system represents an evolution of both the EDEN 41 and MDX 42 systems. CrossVis includes a number of enhancements to the popular parallel coordinate information visualization technique.…”

Section: Data Visualizationmentioning

confidence: 99%

“…For more details on the parallel coordinate-based techniques, the reader is urged to consult the related references. [41][42][43] Fig . 6 The polyline in a parallel coordinates plot maps the Ndimensional data tuple C with coordinates (c 1 , c 2 ,…, c N ) to points on N parallel axes which are joined with a polyline whose N vertices are on the X i -axis for i = 1,…, N…”

Section: Data Visualizationmentioning

confidence: 99%

See 2 more Smart Citations

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

et al. 2019

View full text Add to dashboard Cite

Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

show abstract

Section: Automatic Recognition Of Relevant Features and Extraction Ofmentioning

confidence: 99%

Section: Data Visualizationmentioning

confidence: 99%

Section: Data Visualizationmentioning

confidence: 99%

See 1 more Smart Citation

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Practical applications of parallel coordinates includes pattern recognition [2], measurement processing for intelligent sensor units [3], analysis of financial data [4], enhanced visual analysis of hurricane climate data sets [5] and gene expression data analysis [6]. In the area of monitoring, parallel coordinates have been successfully implemented to monitor temperature variance of a data center and for a wastewater treatment plant [7].…”

Section: Introductionmentioning

confidence: 99%

Use of Principal Components in Parallel Coordinates for Process Monitoring

Nixon

Dunia

Wojsznis

et al. 2013

Artificial Intelligence and Applications / 794: Modelling, Identification and Control / 795: Parallel and Distributed Computing

View full text Add to dashboard Cite

This work demonstrates that parallel coordinates is a recognized visualization technique suitable for process monitoring applications in industrial facilities, where a significant number of sensors are used to detect and identify abnormal operating conditions. Principal Component monitoring methods implemented in Parallel Coordinates, named PC 2 , permits to compare faulty events and determine the frequency of false alarms. The monitoring visualization technology presented by PC 2 was successfully applied to column flooding using actual process data.

show abstract

Improving Performance of Forensics Investigation with Parallel Coordinates Visual Analytics

Wang

Huang

et al. 2014

2014 IEEE 17th International Conference on Computational Science and Engineering

View full text Add to dashboard Cite

Computer forensics investigators aim to analyseand present facts through the examination of digital evidences in short times. As the volume of suspicious data is becoming large, the difficulties of catching the digital evidence in a legally acceptable time are high. This paper proposes an effective method for reducing investigation time redundancy to achieve the normalization of data on hard disk drives (HDD) for computer forensics. We use visualization techniques, parallel coordinates, to analyse data instead of using data analysis algorithms only, and also choose a Red-Black tree structure to de-duplicate data. It reduces the time complexity, including the time spent of searching data, adding data as well as deleting data. We show the advantages of our approach; moreover, we demonstrate how this method can enhance the efficiency and quality of computer forensics task.

show abstract

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

Cited by 3 publications

References 30 publications

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Use of Principal Components in Parallel Coordinates for Process Monitoring

Improving Performance of Forensics Investigation with Parallel Coordinates Visual Analytics

Contact Info

Product

Resources

About