System for 3D visualization and data mining of large vascular trees

2005 ◽  
Author(s):  
Kun-Chang Yu ◽  
Erik L. Ritman ◽  
William E. Higgins
Keyword(s):  
Data Mining ◽  
3D Visualization ◽  
Vascular Trees

Information Visualization with Text Data Mining for Knowledge Discovery Tools in Bioinformatics

2005 ◽  
Vol 277-279 ◽  
pp. 259-265
Author(s):  
Jin Ah Park ◽  
Chang Su Lee ◽  
Jong C. Park
Keyword(s):  
Data Mining ◽  
3D Visualization ◽  
Three Dimensional ◽  
Mitogen Activated Protein Kinase ◽  
Text Data ◽  
Text Data Mining ◽  
Visualization System ◽  
Source Data ◽  
Mitogen Activated Protein ◽  
Available Information

An abundant amount of information is produced in the digital domain, and an effective information extraction (IE) system is required to surf through this sea of information. In this paper, we show that an interactive visualization system works effectively to complement an IE system. In particular, three-dimensional (3D) visualization can turn a data-centric system into a user-centric one by facilitating the human visual system as a powerful pattern recognizer to become a part of the IE cycle. Because information as data is multidimensional in nature, 2D visualization has been the preferred mode. However, we argue that the extra dimension available for us in a 3D mode provides a valuable space where we can pack an orthogonal aspect of the available information. As for candidates of this orthogonal information, we have considered the following two aspects: 1) abstraction of the unstructured source data, and 2) the history line of the discovery process. We have applied our proposal to text data mining in bioinformatics. Through case studies of data mining for molecular interaction in the yeast and mitogen-activated protein kinase pathways, we demonstrate the possibility of interpreting the extracted results with a 3D visualization system.

ChemSpaceShuttle: A tool for data mining in drug discovery by classification, projection, and 3D visualization

2003 ◽  
Vol 22 (5) ◽  
pp. 549-559 ◽  
Author(s):  
Alireza Givehchi ◽  
Axel Dietrich ◽  
Paul Wrede ◽  
Gisbert Schneider
Keyword(s):  
Data Mining ◽  
Drug Discovery ◽  
3D Visualization

scholarly journals Multimodality Data Integration in Epilepsy

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Otto Muzik ◽  
Diane C. Chugani ◽  
Guangyu Zou ◽  
Jing Hua ◽  
Yi Lu ◽  
...  
Keyword(s):  
Data Mining ◽  
Pet Imaging ◽  
3D Visualization ◽  
Underlying Disease ◽  
Spatial Proximity ◽  
Software Environment ◽  
Generic Level ◽  
Efficient Data ◽  
Quantitative Results ◽  
The Relationship

An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG. Based on the developed software tools, quantitative results obtained from individual modalities can be merged into a data structure allowing a consistent framework for advanced data mining techniques and 3D visualization. Moreover, an effort was made to derive quantitative variables (such as the spatial proximity index, SPI) characterizing the relationship between complementing modalities on a more generic level as a prerequisite for efficient data mining strategies. We describe the implementation of this software environment in twelve children (mean age5.2±4.3years) with medically intractable partial epilepsy who underwent both high-resolution structural MR and functional PET imaging. Our experiments demonstrate that our approach will lead to a better understanding of the mechanisms of epileptogenesis and might ultimately have an impact on treatment. Moreover, our software environment holds promise to be useful in many other neurological disorders, where integration of multimodality data is crucial for a better understanding of the underlying disease mechanisms.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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