Real-world datasets, such as genomic data, are noisy and high-dimensional, and are therefore difficult to analyse without a preliminary step aimed to reduce data dimensionality and to select relevant features. Projection techniques are a useful tool to pre-process high dimensional datd since they allow to achieve a simpler representation of the original data that still preserves intrinsic information. In this work, we assess the effectiveness of these methods when applied to two common tasks in Bioinformatics: patient classification and gene clustering. We compared the performance of different learning models in the original space and in several projected spaces obtained with different techniques, both in a supervised and in an unsupervised setting. Our results show that projection techniques can lead to a significant improvement in the learning ability of models.
|Titolo:||Effectiveness of projection techniques in genomic data analysis|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||4.1.1 Proceedings con DOI|