1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	GAN-based training of semi-interpretable generators for biological data interpolation and augmentation
Εναλλακτικός τίτλος:	Εκπαίδευση ημι-ερμηνεύσιμων γεννητριών βασισμένη σε GAN για τη παρεμβολή και την επαύξηση βιολογικών δεδομένων
Συγγραφέας:	[EL] Τσούρτης, Αναστάσιος[EN] Tsourtis, Anastasios [EL] Παπουτσόγλου, Γεώργιος[EN] Papoutsoglou, Georgios [EL] Πανταζής, Ιωάννης[EN] Pantazis, Yannis
Ημερομηνία:	27/05/2022
Περίληψη:	Single-cell measurements incorporate invaluable information regarding the state of each cell and its underlying regulatory mechanisms. The popularity and use of single-cell measurements are constantly growing. Despite the typically large number of collected data, the under-representation of important cell (sub-)populations negatively affects down-stream analysis and its robustness. Therefore, the enrichment of biological datasets with samples that belong to a rare state or manifold is overall advantageous. In this work, we train families of generative models via the minimization of Rényi divergence resulting in an adversarial training framework. Apart from the standard neural network-based models, we propose families of semi-interpretable generative models. The proposed models are further tailored to generate realistic gene expression measurements, whose characteristics include zero-inflation and sparsity, without the need of any data pre-processing. Explicit factors of the data such as measurement time, state or cluster are taken into account by our generative models as conditional variables. We train the proposed conditional models and compare them against the state-of-the-art on a range of synthetic and real datasets and demonstrate their ability to accurately perform data interpolation and augmentation.
Γλώσσα:	Αγγλικά
Σελίδες:	16
DOI:	10.3390/app12115434
EISSN:	2076-3417
Θεματική κατηγορία:	[EL] Βιοπληροφορική[EN] Bioinformatics
Λέξεις-κλειδιά:	single-cell RNA-seq data generation; data interpolation and augmentation; Generative adversarial networks; Rényi divergence minimization; zero-inflated random variables; Gaussian mixture model
Κάτοχος πνευματικών δικαιωμάτων:	© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Όροι και προϋποθέσεις δικαιωμάτων:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.mdpi.com/2076-3417/12/11/5434
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.mdpi.com/journal/applsci
Τίτλος πηγής δημοσίευσης:	Applied Sciences
Τεύχος:	11
Τόμος:	12
Σελίδες τεκμηρίου (στην πηγή):	Article no 5434
Σημειώσεις:	This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014–2020” in the context of the project “Characterizing Population Dynamics with Applications in Biological Data” (MIS 5050686). Yannis Pantazis acknowledges partial support by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “Second Call for H.F.R.I. Research Projects to support Faculty members and Researchers” (Project Number: 4753).
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες