Εξειδίκευση τύπου :	Ανακοίνωση σε συνέδριο
Τίτλος:	Combining pathway analysis and supervised machine learning for the functional classification of single-cell transcriptomic data
Δημιουργός/Συγγραφέας:	Koutsandreas T. Bajram A. Mastrokalou C. Pilalis E. [EL] Χατζηιωάννου, Αριστοτέλης[EN] Chatziioannou, Aristotelis Maglogiannis I.
Εκδότης:	Institute of Electrical and Electronics Engineers Inc.
Ημερομηνία:	2019
Γλώσσα:	Αγγλικά
ISBN:	9781728146171
DOI:	10.1109/BIBE.2019.00160
Περίληψη:	The revolution of single-cell technologies established a novel framework to investigate gene expression profiles in the level of individual cells. Scientists are able to investigate the biological variability of the same tissue, producing isolated transcriptomic data for each single cell. As a result, each transcriptomic experiment could extract a unique expression profile for each cell, posing new challenges in the translation analysis of all these profiles. Pathway analysis tools need to be adapted, not only to analyze simultaneously numerous gene expression profiles, but also to compare them, detecting functional differences and commonalities among the cells of the same issue, separating them to functional subclusters. In this study, we used the output of a single-cell experiment in the hematopoietic system, in order to determine a novel framework for the functional comparison of single cells, based on their pathway analysis with Gene Ontology annotation. Thousands of expression profiles of single cells, congregated in 15 different hematopoietic classes, were translated into networks of significant biological mechanisms, through the use of BioInfoMiner platform. We propose a novel framework to exploit these results and construct appropriate feature spaces of functional omponents, with a view to perform supervised learning to different hematopoietic cell types and separate their respective single cells, according to their functional profile. The constructed classification model performed interestingly high precision and sensitivity scores for some cell types, while the overall performance needs to be improved with further conceptual and technical refinements.
Όνομα εκδήλωσης:	19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Ημ/νία έναρξης εκδήλωσης :	2019-10-28
Ημ/νία λήξης εκδήλωσης :	2019-10-30
Τίτλος πηγής δημοσίευσης:	Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Σελίδες:	861-866
Θεματική Κατηγορία:	[EL] Χημεία (Γενικά)[EN] Chemistry (General)
Λέξεις-Κλειδιά:	Classification Hematopoietic cells Pathway analysis Semantic analysis Single-cell experiment Supervised machine learning
Αξιολόγηση από ομότιμους (peer reviewed):	Ναι
Κάτοχος πνευματικών δικαιωμάτων:	© 2019 IEEE.
Σημειώσεις:	Ministry of Education, Lifelong Learning and Religious Affairs: MIS 5017608; European Regional Development Fund, FEDER. This work was funded by the Operational Program “Competitiveness, Entrepreneurship and Innovation 2014– 2020” (co-funded by the European Regional Development Fund) and managed by the General Secretariat of Research and Technology, Ministry of Education, Research and Religious Affairs, under the project “Innovative Nanopharmaceuticals: Targeting Breast Cancer Stem Cells by a Novel Combination of Epigenetic and Anticancer Drugs with Gene Therapy (INNOCENT) (MIS 5017608)” (7th Joint Translational Call – 2016, European Innovative Research and Technological Development Projects in Nanomedicine) of the ERA-NET EuroNanoMed II. This work was funded by the Operational Program Competitiveness, Entrepreneurship and Innovation 2014-2020 (co-funded by the European Regional Development Fund) and managed by the General Secretariat of Research and Technology, Ministry of Education, Research and Religious Affairs, under the project Innovative Nanopharmaceuticals: Targeting Breast Cancer Stem Cells by a Novel Combination of Epigenetic and Anticancer Drugs with Gene Therapy (INNOCENT) (MIS 5017608) (7th Joint Translational Call - 2016, European Innovative Research and Technological Development Projects in Nanomedicine) of the ERA-NET EuroNanoMed II
Εμφανίζεται στις συλλογές:	Ινστιτούτο Χημικής Βιολογίας - Επιστημονικό έργο