(2021). Recent Dimensionality Reduction Techniques for Visualizing High-Dimensional Parkinson’s Disease Omics Data. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4460–4463). IEEE. https://doi.org/10.1109/bigdata52589.2021.9671736
Περίληψη
One challenge facing Systems Biology is the conversion of vast amounts of data into systematic and organized knowledge through automated processes. Improvements in experimental technologies have created an enormous pool of heterogeneous omics data such as genomics, proteomics and metabolomics. Exporting insights of these datasets can lead to important discoveries for complex pathologies such as age-related neurodegenerative disorders and specifically Parkinson's disease (PD). However, such data are characterized by huge dimensionality which increases their complexity for various data analyses as well for data mining processes such as clustering and classification. In this perspective, we implemented state-of-the-art Dimensionality Reduction Techniques for Visualizing Omics High-Dimensional Parkinson’s Disease Data. Our study highlights the cutting-edge dimensionality reduction techniques for 2D data visualization and their contribution the deeper interpretation of PD data. Approaches in this direction can provide a deeper understanding of biological dynamics and enable integrative multilayered diagnostic assessment of complex disorders such as neurodegenerative diseases.
- DOI
- 10.1109/bigdata52589.2021.9671736
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2021
Σύνδεσμοι
BibTeX
@inproceedings{krokidis2021recent,
title = {Recent Dimensionality Reduction Techniques for Visualizing High-Dimensional Parkinson’s Disease Omics Data},
author = {Marios G. Krokidis and Georgios N. Dimitrakopoulos and Aristidis G. Vrahatis and Themis P. Exarchos and Panagiotis Vlamos},
url = {https://doi.org/10.1109/bigdata52589.2021.9671736},
doi = {10.1109/bigdata52589.2021.9671736},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE International Conference on Big Data (Big Data)},
volume = {10},
pages = {4460–4463},
publisher = {IEEE},
abstract = {One challenge facing Systems Biology is the conversion of vast amounts of data into systematic and organized knowledge through automated processes. Improvements in experimental technologies have created an enormous pool of heterogeneous omics data such as genomics, proteomics and metabolomics. Exporting insights of these datasets can lead to important discoveries for complex pathologies such as age-related neurodegenerative disorders and specifically Parkinson's disease (PD). However, such data are characterized by huge dimensionality which increases their complexity for various data analyses as well for data mining processes such as clustering and classification. In this perspective, we implemented state-of-the-art Dimensionality Reduction Techniques for Visualizing Omics High-Dimensional Parkinson’s Disease Data. Our study highlights the cutting-edge dimensionality reduction techniques for 2D data visualization and their contribution the deeper interpretation of PD data. Approaches in this direction can provide a deeper understanding of biological dynamics and enable integrative multilayered diagnostic assessment of complex disorders such as neurodegenerative diseases.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}