AIMC Topic: Computational Biology
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A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.
PloS one
Apr 25, 2025
The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches ofte...
Specific expression and common potential therapeutic drugs in different brain regions of major depressive disorder patients: bioinformatics analysis.
Journal of affective disorders
Apr 24, 2025
Major depressive disorder (MDD) is a prevalent and debilitating mental health condition characterized by persistent feelings of sadness and loss of interest. Despite its high prevalence, the underlying molecular mechanisms remain poorly understood. T...
TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.
Computer methods and programs in biomedicine
Apr 24, 2025
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, co...
UBTD2 protein molecules emerges as a key prognostic protein marker in glioma: Insights from integrated omics and machine learning analysis of GRM7, NCAPG, CEP55, and other biomarkers.
International journal of biological macromolecules
Apr 24, 2025
Glioma is a malignant brain tumor with poor prognosis, and there is an urgent need to find effective biomarkers for early diagnosis and treatment. The aim of this study was to explore the potential of UBTD2 as a key prognostic protein marker for glio...
Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning.
Scientific reports
Apr 24, 2025
Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate the potential therapeutic targets post-SAH, the SAH-related feature genes were screened by the comb...
Identification and verification of mitochondria-related genes biomarkers associated with immune infiltration for COPD using WGCNA and machine learning algorithms.
Scientific reports
Apr 24, 2025
Mitochondrial dysfunction plays a pivotal role in the pathogenesis of chronic obstructive pulmonary disease (COPD). This study combines bioinformatics analysis with machine learning to elucidate potential key mitochondrial-related genes associated wi...
Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis.
Scientific reports
Apr 24, 2025
Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein, hub genes and regulatory mechanisms in sepsis was investigated. The GSE65682 and GSE95233 datasets ...
Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.
PloS one
Apr 24, 2025
Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions s...
Pato: prediction of probiotic bacteria using metabolic features.
Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
Apr 23, 2025
Probiotics have gained recognition for their health-promoting benefits, particularly in the gastrointestinal and immunological systems. Among promising probiotic candidates, Lactobacillus strains, belonging to the lactic acid bacteria (LAB) group, pl...
A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.
Scientific reports
Apr 23, 2025
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...
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