Publications by authors named "F Bui"
Aim: Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.
Methods: Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications.
RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning-based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets.
View Article and Find Full Text PDFArticle Synopsis
- Infectious fungi pose a growing global threat, and using Antifungal peptides (AFP) is a promising way to create effective antifungal drugs with minimal toxicity to hosts.
- The study introduces MLAFP-XN, a neural network-based approach that accurately identifies active AFP in sequencing data, employing eight feature extraction techniques along with the XGB feature selection strategy.
- After evaluating 24 classification models, the top four achieved impressive accuracy rates, outperforming existing methods, and a companion website was created to showcase the AFP recognition process and highlight influential properties using SHAP.
Background And Aims: The single nucleotide polymorphisms (SNPs) in gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human gene using various computational techniques.
Materials And Methods: Several tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.
Aims: The main objective of the current study is to investigate the pathways and therapeutic targets linked to stevioside in the management of T2D using computational approaches.
Methods: We collected RNA-seq datasets from NCBI, then employed GREIN to retrieve differentially expressed genes (DEGs). Computer-assisted techniques DAVID, STRING and NetworkAnalyst were used to explore common significant pathways and therapeutic targets associated with T2D and stevioside.