Hachim, MY;
Mahboub, B;
Hamid, Q;
Hamoudi, R;
(2019)
Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using Image Filtering Algorithms.
In:
Proceedings of 2019 IEEE International Conference on Imaging Systems and Techniques (IST).
IEEE: Abu Dhabi, United Arab Emirates.
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Abstract
Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using Image Filtering Algorithms |
| Event: | 2019 IEEE International Conference on Imaging Systems and Techniques (IST) |
| ISBN-13: | 9781728138688 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IST48021.2019.9010412 |
| Publisher version: | http://dx.doi.org/10.1109/IST48021.2019.9010412 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | microarray, image analysis, transcriptomics, normalization, coefficient of variance, Respiratory system, Diseases, Probes, Gene expression, Pipelines, Bioinformatics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10104658 |
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