Effective Enrichment of Gene Expression Data Sets

Sirin, Utku
Erdogdu, Utku
Polat, Faruk
Alhajj, Reda
The ever-growing need for gene-expression data analysis motivates studies in sample generation due to the lack of enough gene-expression data. It is common that there are thousands of genes but only tens or rarely hundreds of samples available. In this paper, we attempt to formulate the sample generation task as follows: first, building alternative Gene Regulatory Network (GRN) models; second, sampling data from each of them; and then filtering the generated samples using metrics that measure compatibility, diversity and coverage with respect to the original dataset. We constructed two alternative GRN models using Probabilistic Boolean Networks and Ordinary Differential Equations. We developed a multi-objective filtering mechanism based on the three metrics to assess the quality of the newly generated data. We presented a number of experiments to show effectiveness and applicability of the proposed multi-model framework.


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End User Evaluation of the FAIR4Health Data Curation Tool
Gencturk, Mert; Teoman, Alper; Alvarez-Romero, Celia; Martinez-Garcia, Alicia; Parra-Calderon, Carlos Luis; Poblador-Plou, Beatriz; Löbe, Matthias; Sinaci, A Anil (2021-05-27)
The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method. We applied a series of questionnaires to a group of experts not only in different roles and skills, but also from various...
Citation Formats
U. Sirin, U. Erdogdu, M. TAN, F. Polat, and R. Alhajj, “Effective Enrichment of Gene Expression Data Sets,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40677.