Analysis of normalization method for DNA microarray data

Baans, Omar Salem; Jambek, Asral Bahari; Said, Khairul Anuar Mat

doi:10.35118/apjmbb.2019.027.4.04

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quantile normalization is also used for single color Agilent data (Smyth, 2005). Loess is a local polynomial regression-based approach which can be utilized to adjust intensity levels between two channels (Yang et al, 2002;Smyth and Speed, 2003;Bullard et al, 2010;Baans et al, 2017). Loess normalization performs local regression for each pair of arrays which are composed of the difference and average of the log-transformed intensities derived from the two channels.…”

Section: Data Normalizationmentioning

confidence: 99%

Adoption of Artificial Intelligence in Human and Clinical Genomics

Jain,

Zhang,

Zhang

et al. 2023

Frontiers Research Topics

View full text Add to dashboard Cite

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

Section: Data Normalizationmentioning

confidence: 99%

Adoption of Artificial Intelligence in Human and Clinical Genomics

Jain,

Zhang,

Zhang

et al. 2023

Frontiers Research Topics

View full text Add to dashboard Cite

show abstract

GENE MARKERS IDENTIFICATION OF ACUTE MYOCARDIAL INFARCTION DISEASE BASED ON GENOMIC PROFILING THROUGH EXTREME GRADIENT BOOSTING (XGBoost)

Fajriyah,

Isnandar,

Arifuddin

2024

Medstat

View full text Add to dashboard Cite

One disease that can cause death is Acute Myocardial Infarction (AMI). AMI, also known as a heart attack, is a condition that causes permanent damage to heart muscle tissue due to prolonged ischemia or lack of blood flow that occurs due to blockage of the epicardial coronary arteries and results in blood clots and limiting blood supply to the myocardium. During the years the young AMI patients are increasing. One of the ways to diagnose early is providing information of biomarkers related to this disease by implementing the bioinformatics data analysis. The research was conducted to look at the genomic profile of patients suffering from AMI based on without recurrent events and normal control, using the XGBoost method, due to its scalability and efficiency. Based on the grid search of tuning hyperparameters, the XGBoost method gives a classification accuracy of 88.89%, AUC 90 and kappa 0.7805. These results indicate that the XGBoost method can classify patients suffering from AMI well. This research has identified three genes that contribute the most to classifying AMI patients, namely calponin 2, ribosomal protein S11 and myotropin. Based on the heatmap visualization, information was obtained that the three genes are class markers without recurrent events.

show abstract

Analysis of normalization method for DNA microarray data

Cited by 2 publications

References 10 publications

Adoption of Artificial Intelligence in Human and Clinical Genomics

Adoption of Artificial Intelligence in Human and Clinical Genomics

GENE MARKERS IDENTIFICATION OF ACUTE MYOCARDIAL INFARCTION DISEASE BASED ON GENOMIC PROFILING THROUGH EXTREME GRADIENT BOOSTING (XGBoost)

Contact Info

Product

Resources

About