[HTML][HTML] Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

R Rocca, K Grillone, EL Citriniti, G Gualtieri… - European Journal of …, 2023 - Elsevier
The growing information currently available on the central role of non-coding RNAs
(ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic …

Overcoming low adherence to chronic medications by improving their effectiveness using a personalized second-generation digital system

A Bayatra, R Nasserat, Y Ilan - Current Pharmaceutical …, 2024 - benthamdirect.com
Introduction: Low adherence to chronic treatment regimens is a significant barrier to
improving clinical outcomes in patients with chronic diseases. Low adherence is a result of …

AMDECDA: attention mechanism combined with data ensemble strategy for predicting CircRNA-disease association

L Wang, L Wong, ZH You… - IEEE Transactions on Big …, 2023 - ieeexplore.ieee.org
Accumulating evidence from recent research reveals that circRNA is tightly bound to human
complex disease and plays an important regulatory role in disease progression. Identifying …

BCMCMI: a fusion model for predicting circRNA-miRNA interactions combining semantic and meta-path

MM Wei, CQ Yu, LP Li, ZH You… - Journal of Chemical …, 2023 - ACS Publications
More and more evidence suggests that circRNA plays a vital role in generating and treating
diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA …

GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47

W Shan, L Chen, H Xu, Q Zhong, Y Xu, H Yao… - Frontiers in …, 2023 - frontiersin.org
Identifying compound–protein interaction plays a vital role in drug discovery. Artificial
intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are …

Graph reasoning method based on affinity identification and representation decoupling for predicting lncRNA-disease associations

S Wang, C Hui, T Zhang, P Wu… - Journal of Chemical …, 2023 - ACS Publications
An increasing number of studies have shown that dysregulation of lncRNAs is related to the
occurrence of various diseases. Most of the previous methods, however, are designed …

An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP

N Almusallam, F Ali, A Masmoudi, SA Ghazalah… - International Journal of …, 2024 - Elsevier
Angiogenic proteins (AGPs) play a critical role in both pathological and physiological
activities, making them key therapeutic targets in diseases like cancer, heart disease, and …

GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features

J Mi, H Wang, J Li, J Sun, C Li, J Wan… - Briefings in …, 2024 - academic.oup.com
Recent advances in high-throughput sequencing have led to an explosion of genomic and
transcriptomic data, offering a wealth of protein sequence information. However, the …

Machine learning strategies in microRNA research: bridging genome to phenome

S Daniel Thomas, K Vijayakumar, L John… - OMICS: A Journal of …, 2024 - liebertpub.com
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression.
This article offers the salient and current aspects of machine learning (ML) tools and …

AntiViralDL: Computational Antiviral Drug Repurposing Using Graph Neural Network and Self-Supervised Learning

P Zhang, X Hu, G Li, L Deng - IEEE Journal of Biomedical and …, 2023 - ieeexplore.ieee.org
Viral infections have emerged as significant public health concerns for decades. Antiviral
drugs, specifically designed to combat these infections, have the potential to reduce the …