Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data
Date
2021-09-01
Author
Kutlay, Aysegul
Aydın Son, Yeşim
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
192
views
0
downloads
Cite This
Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation.
URI
https://hdl.handle.net/11511/94453
Journal
FRONTIERS IN MOLECULAR BIOSCIENCES
DOI
https://doi.org/10.3389/fmolb.2021.637355
Collections
Graduate School of Informatics, Article
Suggestions
OpenMETU
Core
Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning
Polat, Gorkem; Kani, Haluk Tarik; Ergenc, Ilkay; Alahdab, Yesim Ozen; Temizel, Alptekin; Atug, Ozlen (2022-11-01)
Background Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment. Methods The cohort comprises 11 276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep ...
Incorporation of personal Single Nucleotide Polymorphism (SNP) data into a national level electronic health record for disease risk assessment, Part 3: An evaluation of SNP Incorporated National Health Information System of Turkey for prostate cancer
Beyan, Timur; Aydın Son, Yeşim (2014-08-01)
©Timur Beyan, Yeşim Aydin Son.Background: A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various centr...
Incorporation of personal Single Nucleotide Polymorphism (SNP) data into a national level electronic health record for disease risk assessment, part 2: The incorporation of SNP into the national health information system of Turkey
Beyan, Timur; Aydın Son, Yeşim (2014-08-01)
©Timur Beyan, Yeşim Aydin Son.Background: A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical record (EMR)s/electronic health record (EHR)s systems. Today, various...
Leveraging the Molecular Signatures of Cancer for Dynamic Network Modeling
Tunçbağ , Nurcan; Ayar , Enes Sefa (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10)
Molecular heterogeneity and resistance to the treatment are among the obstacles in developing treatment strategies in cancer. Therefore, transforming patient-specific molecular data into clinically interpretable knowledge is fundamental in personalized medicine. However, not all molecular alterations drive cancer. The distinction of drivers from latent drivers and passengers, their cooperativity and exclusivity, and the temporal order of accumulation of molecular alterations is a crucial yet daunting, unsol...
Differential immune activation following encapsulation of immunostimulatory CpG oligodeoxynucleotide in nanoliposomes.
Erikçi, E; Gürsel, Mayda; Gürsel, I (2011-02-01)
The immunogenicity of a vaccine formulation is closely related to the effective internalization by the innate immune cells that provide prolonged and simultaneous delivery of antigen and adjuvant to relevant antigen presenting cells. Endosome associated TLR9 recognizes microbial unmethylated CpG DNA. Clinical applications of TLR9 ligands are significantly hampered due to their pre-mature in vivo digestion and rapid clearance. Liposome encapsulation is a powerful tool to increase in vivo stability as well as...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. Kutlay and Y. Aydın Son, “Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data,”
FRONTIERS IN MOLECULAR BIOSCIENCES
, vol. 8, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94453.