Effectiveness of projection techniques in genomic data analysis

Paola Galdi*, Angela Serra, Dario Greco, Roberto Tagliaferri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509011315
DOIs
Publication statusPublished - 9 Nov 2016
Externally publishedYes
Event2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016 - Bologna, Italy
Duration: 7 Sep 20169 Sep 2016

Conference

Conference2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow, RTSI 2016
Country/TerritoryItaly
CityBologna
Period7/09/169/09/16

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