Application of Attention and Graph Transformer-Based Approaches for RNA Biomarker Discovery in Metabolically-Associated Fatty Liver Disease (MAFL/NASH)
bioRxiv, ISSN: 2692-8205
2023
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Mentions1
- News Mentions1
- News1
Most Recent News
Application of Attention and Graph Transformer-Based Approaches for RNA Biomarker Discovery in Metabolically-Associated Fatty Liver Disease (MAFL/NASH)
2023 NOV 20 (NewsRx) -- By a News Reporter-Staff News Editor at Disease Prevention Daily -- According to news reporting based on a preprint abstract,
Article Description
The prevalence of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) in the United States has reached epidemic proportions, increasing the risk of liver cirrhosis and cancer. Current methods of diagnosis for NAFLD/NASH are invasive and costly, motivating the need for genetic “RNA” biomarkers detectable in a blood sample. In this study, explainable artificial intelligence (XAI) techniques are employed to increase the interpretability of the deep learning models in detecting the potential mRNA biomarker candidates for NAFLD/NASH. Nine RNA datasets (~1000 patients) with NAFLD/NASH were collected from the Gene Expression Omnibus. After conducting a differential gene expression analysis to reduce the dimensionality of the expression data, single-head and multi-head attention models were compared to baseline machine learning models in their ability to classify patients as NAFLD/NASH/healthy. XAI methods, including L1 regularization on baseline models and analysis of the internal attention matrix of the attention models, were utilized to identify biomarker candidates based on the relative importance of genes. The attention models achieved superior performance (accuracy: 67.5%) compared to the baseline models (Negative Binomial Linear Discriminant Analysis-62.64%; Poisson Linear Discriminant Analysis with Power Transformation – 58.24%). The top 17 and top 20 XAI-identified biomarkers with the baseline machine learning algorithms and the attention-based models respectively were then evaluated in lab. Preliminary data from in-lab validation confirmed upregulation of MT-ND3, HLA-B, APOC-1, and APOL-1 in NAFLD/NASH patients. Attention models have shown promise in identifying expression-based mRNA biomarkers and accurately diagnosing patients with NAFLD/NASH.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know