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Authors: Ivan Y. Iourov 1 ; Svetlana G. Vorsanova 2 and Yuri B. Yurov 3

Affiliations: 1 Mental Health Research Center, Separated Structural Unit “Clinical Research Institute of Pediatrics” named after Y.E. Veltishev,Russian National Research Medical University named after N.I. Pirogov, Ministry of Health of Russian Federation and Russian Medical Academy of Postgraduate Education, Russian Federation ; 2 Separated Structural Unit “Clinical Research Institute of Pediatrics” named after Y.E. Veltishev,Russian National Research Medical University named after N.I. Pirogov and Ministry of Health of Russian Federation, Russian Federation ; 3 Mental Health Research Center, Separated Structural Unit “Clinical Research Institute of Pediatrics” named after Y.E. Veltishev,Russian National Research Medical University named after N.I. Pirogov and Ministry of Health of Russian Federation, Russian Federation

ISBN: 978-989-758-280-6

ISSN: 2184-4305

Keyword(s): Brain Diseases, Clinical Relevance, Genomic Variations, Interpretation Technologies, Molecular Diagnosis, Neurogenomics, Systems Biology.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Genomics and Proteomics ; Structural Variations ; Systems Biology

Abstract: Biotechnological advances in genomics have significantly impacted on molecular diagnosis. As a result, uncovering individual genomic variations has made whole-genome analysis attractive for clinical care of patients suffering from brain diseases. However, to obtain clinically relevant genomic data for successful molecular genetic/genomic diagnosis, interpretation technologies are recognized to be indispensable. Taking into account the predictive power of bioinformatics in basic genetic studies, it has been proposed to use in silico systems biology analysis and data mining for detecting clinically relevant genomic variations by diagnostic healthcare services. Here, we describe an algorithm used as an integral part of molecular diagnosis of clinically relevant genomic pathology (neurogenomic variations) in brain diseases. The bioinformatic technique allows interpreting variations at chromosome and gene levels through systems biology analysis including literature data mining, which enabl es to modulate the effect of each genomic change at transcriptome, proteome and metabolome levels. Studying neurogenomic variations using this approach, we were able to show that the algorithm can be used as a valuable add-on to whole genome analysis for diagnostic purposes inasmuch as it appreciably increases the efficiency of molecular diagnosis. (More)

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Paper citation in several formats:
Iourov, I.; Vorsanova, S. and Yurov, Y. (2018). Systems Biology Analysis and Literature Data Mining for Unmasking Pathogenic Neurogenomic Variations in Clinical Molecular Diagnosis.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS, ISBN 978-989-758-280-6, ISSN 2184-4305, pages 160-165. DOI: 10.5220/0006649701600165

@conference{bioinformatics18,
author={Ivan Y. Iourov. and Svetlana G. Vorsanova. and Yuri B. Yurov.},
title={Systems Biology Analysis and Literature Data Mining for Unmasking Pathogenic Neurogenomic Variations in Clinical Molecular Diagnosis},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS,},
year={2018},
pages={160-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006649701600165},
isbn={978-989-758-280-6},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS,
TI - Systems Biology Analysis and Literature Data Mining for Unmasking Pathogenic Neurogenomic Variations in Clinical Molecular Diagnosis
SN - 978-989-758-280-6
AU - Iourov, I.
AU - Vorsanova, S.
AU - Yurov, Y.
PY - 2018
SP - 160
EP - 165
DO - 10.5220/0006649701600165

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