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Articles 1 - 4 of 4
Full-Text Articles in Statistical Models
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Computer Science ETDs
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
The Summer Undergraduate Research Fellowship (SURF) Symposium
Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning …
A Gene-Based Association Method For Mapping Traits Using Reference Transcriptome Data, Eric R. Gamazon, Heather Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im
A Gene-Based Association Method For Mapping Traits Using Reference Transcriptome Data, Eric R. Gamazon, Heather Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im
Heather Wheeler
Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates ‘imputed’ gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys …
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
COBRA Preprint Series
Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …