Szanowni Państwo, w związku z bardzo dużą ilością zgłoszeń, rejestracją danych w dwóch systemach bibliograficznych, a jednocześnie zmniejszonym zespołem redakcyjnym proces rejestracji i redakcji opisów publikacji jest wydłużony. Bardzo przepraszamy za wszelkie niedogodności i dziękujemy za Państwa wyrozumiałość.
Repository logoRepository logoRepository logoRepository logo
Repository logoRepository logoRepository logoRepository logo
  • Communities & Collections
  • Research Outputs
  • Employees
  • AAAHigh contrastHigh contrast
    EN PL
    • Log In
      Have you forgotten your password?
AAAHigh contrastHigh contrast
EN PL
  • Log In
    Have you forgotten your password?
  1. Home
  2. Bibliografia UPP
  3. Bibliografia UPP
  4. Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method
 
Full item page
Options

Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method

Type
Journal article
Language
English
Date issued
2023
Author
Eren, Barış
Türkoğlu, Aras
Haliloğlu, Kamil
Demirel, Fatih
Nowosad, Kamila
Özkan, Güller
Niedbała, Gniewko 
Pour-Aboughadareh, Alireza
Bujak, Henryk
Bocianowski, Jan 
Faculty
Wydział Rolnictwa, Ogrodnictwa i Biotechnologii
Wydział Inżynierii Środowiska i Inżynierii Mechanicznej
Journal
Plants
ISSN
2223-7747
DOI
10.3390/plants12183261
Web address
https://www.mdpi.com/2223-7747/12/18/3261
Volume
12
Number
18
Pages from-to
art. 3261
Abstract (EN)
Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion–iPBS (CRED–iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED–iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.
Keywords (EN)
  • DNA methylation

  • genomic template stability

  • iPBS

  • machine learning

License
cc-bycc-by CC-BY - Attribution
Open access date
September 13, 2023
Fundusze Europejskie
  • About repository
  • Contact
  • Privacy policy
  • Cookies

Copyright 2025 Uniwersytet Przyrodniczy w Poznaniu

DSpace Software provided by PCG Academia