FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2330892731> ?p ?o ?g. }

• W2330892731 abstract "In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g. brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples including 1) quality control measures calculated from phantom data over time, 2) quality control data from human functional MRI data across various studies, scanners, sites, 3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e. data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger." @default.
• W2330892731 created "2016-06-24" @default.
• W2330892731 creator A5005182829 @default.
• W2330892731 creator A5006154621 @default.
• W2330892731 creator A5015647568 @default.
• W2330892731 creator A5020491182 @default.
• W2330892731 creator A5022649723 @default.
• W2330892731 creator A5027310217 @default.
• W2330892731 creator A5032107528 @default.
• W2330892731 creator A5032850756 @default.
• W2330892731 creator A5039319188 @default.
• W2330892731 creator A5046014044 @default.
• W2330892731 creator A5053332249 @default.
• W2330892731 creator A5060224736 @default.
• W2330892731 creator A5082230429 @default.
• W2330892731 date "2016-03-15" @default.
• W2330892731 modified "2023-10-16" @default.
• W2330892731 title "A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets" @default.
• W2330892731 cites W1492947634 @default.
• W2330892731 cites W1523421742 @default.
• W2330892731 cites W1560723556 @default.
• W2330892731 cites W1972498024 @default.
• W2330892731 cites W1980527160 @default.
• W2330892731 cites W1980553836 @default.
• W2330892731 cites W1983366858 @default.
• W2330892731 cites W1988979669 @default.
• W2330892731 cites W2003545984 @default.
• W2330892731 cites W2004388578 @default.
• W2330892731 cites W2020519533 @default.
• W2330892731 cites W2027237000 @default.
• W2330892731 cites W2034089470 @default.
• W2330892731 cites W2034406508 @default.
• W2330892731 cites W2073092061 @default.
• W2330892731 cites W2092826400 @default.
• W2330892731 cites W2093745477 @default.
• W2330892731 cites W2108395352 @default.
• W2330892731 cites W2112434324 @default.
• W2330892731 cites W2119848633 @default.
• W2330892731 cites W2131346614 @default.
• W2330892731 cites W2132782405 @default.
• W2330892731 cites W2144262351 @default.
• W2330892731 cites W2161671092 @default.
• W2330892731 cites W2167265771 @default.
• W2330892731 cites W2167868121 @default.
• W2330892731 cites W2243210237 @default.
• W2330892731 cites W361253298 @default.
• W2330892731 cites W4205779493 @default.
• W2330892731 cites W4230920194 @default.
• W2330892731 doi "https://doi.org/10.3389/fninf.2016.00009" @default.
• W2330892731 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4791544" @default.
• W2330892731 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27014049" @default.
• W2330892731 hasPublicationYear "2016" @default.
• W2330892731 type Work @default.
• W2330892731 sameAs 2330892731 @default.
• W2330892731 citedByCount "21" @default.
• W2330892731 countsByYear W23308927312016 @default.
• W2330892731 countsByYear W23308927312017 @default.
• W2330892731 countsByYear W23308927312018 @default.
• W2330892731 countsByYear W23308927312019 @default.
• W2330892731 countsByYear W23308927312020 @default.
• W2330892731 countsByYear W23308927312021 @default.
• W2330892731 countsByYear W23308927312022 @default.
• W2330892731 countsByYear W23308927312023 @default.
• W2330892731 crossrefType "journal-article" @default.
• W2330892731 hasAuthorship W2330892731A5005182829 @default.
• W2330892731 hasAuthorship W2330892731A5006154621 @default.
• W2330892731 hasAuthorship W2330892731A5015647568 @default.
• W2330892731 hasAuthorship W2330892731A5020491182 @default.
• W2330892731 hasAuthorship W2330892731A5022649723 @default.
• W2330892731 hasAuthorship W2330892731A5027310217 @default.
• W2330892731 hasAuthorship W2330892731A5032107528 @default.
• W2330892731 hasAuthorship W2330892731A5032850756 @default.
• W2330892731 hasAuthorship W2330892731A5039319188 @default.
• W2330892731 hasAuthorship W2330892731A5046014044 @default.
• W2330892731 hasAuthorship W2330892731A5053332249 @default.
• W2330892731 hasAuthorship W2330892731A5060224736 @default.
• W2330892731 hasAuthorship W2330892731A5082230429 @default.
• W2330892731 hasBestOaLocation W23308927311 @default.
• W2330892731 hasConcept C104293457 @default.
• W2330892731 hasConcept C124101348 @default.
• W2330892731 hasConcept C126838900 @default.
• W2330892731 hasConcept C154945302 @default.
• W2330892731 hasConcept C162324750 @default.
• W2330892731 hasConcept C172367668 @default.
• W2330892731 hasConcept C176217482 @default.
• W2330892731 hasConcept C177264268 @default.
• W2330892731 hasConcept C199360897 @default.
• W2330892731 hasConcept C21547014 @default.
• W2330892731 hasConcept C24756922 @default.
• W2330892731 hasConcept C36464697 @default.
• W2330892731 hasConcept C41008148 @default.
• W2330892731 hasConcept C41608201 @default.
• W2330892731 hasConcept C54170458 @default.
• W2330892731 hasConcept C58489278 @default.
• W2330892731 hasConcept C71924100 @default.
• W2330892731 hasConcept C77331912 @default.
• W2330892731 hasConceptScore W2330892731C104293457 @default.
• W2330892731 hasConceptScore W2330892731C124101348 @default.
• W2330892731 hasConceptScore W2330892731C126838900 @default.
• W2330892731 hasConceptScore W2330892731C154945302 @default.

• next