Network gene function prediction

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods...

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer...

The N6 -methyladenosine reader YTH N6 -methyladenosine RNA binding protein 1 (YTHDF1) has been assessed in several tumor types and holds significance in the tumor microenvironment (TME). Furthermore, p53, an important tumor suppressor, is closely associated with the TME. The present study evaluated the roles of YTHDF1 and p53 in regulating the TME in gastric cancer (GC). Genetic alterations in the YTH domain family were analyzed using the cBioPortal database. Expression of YTHDF1 in GC cells and tissues was assessed using the Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham Cancer data analysis portal and Tumor-Immune System Interactions and Drug Bank (TISIDB) databases, along with reverse-transcription-quantitative PCR and western blotting in GC...

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs...

Cardiovascular diseases (CVDs) are significant causes of morbidity and mortality worldwide, and pathological cardiac hypertrophy (PCH) is an essential predictor of many heart diseases. Guanxinshutong capsule (GXST) is a Chinese patent medicine widely used in the clinical treatment of CVD, In our previous research, we identified 111 compounds of GXST. In order to reveal the potential molecular mechanisms by which GXST treats PCH, this study employed network pharmacology methods to screen for the active ingredients of GXST in treating PCH and predicted the potential targets...

Non-alcoholic fatty liver disease (NAFLD), a chronic liver condition and metabolic disorder, has emerged as a significant health issue worldwide. D-mannose, a natural monosaccharide widely existing in plants and animals, has demonstrated metabolic regulatory properties. However, the effect and mechanism by which D-mannose may counteract NAFLD have not been studied. In this study, network pharmacology followed by molecular docking analysis was utilized to identify potential targets of mannose against NAFLD, and the leptin receptor-deficient, genetically obese db/db mice was employed as an animal model of NAFLD to validate the regulation of D-mannose on core targets...

The purpose of this study was to screen differentially expressed genes in PCOS using gene chip data and investigate the biological functions of these DEGs in PCOS. Additionally, the study aimed to analyze the potential clinical significance of these genes using clinical data. In this study, we first screened the DEGs related to PCOS by using the gene chip data (GSE5090) from GEO database. Target gene prediction software was used to predict the target genes for these DEGs, and their functional enrichment was analysed...

Excessive inflammatory response following ischemic stroke (IS) injury is a key factor affecting the functional recovery of patients. The efferocytic clearance of apoptotic cells within ischemic brain tissue is a critical mechanism for mitigating inflammation, presenting a promising avenue for the treatment of ischemic stroke. However, the cellular and molecular mechanisms underlying efferocytosis in the brain after IS and its impact on brain injury and recovery are poorly understood. This study explored the roles of inflammation and efferocytosis in IS with bioinformatics...

Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions...

MOTIVATION: Elucidating protein function is a central problem in biochemistry, genetics, and molecular biology. Developing computational methods for protein function prediction is critical due to the significant gap between sequence and functional data. Recent advances in protein structure prediction, which strongly correlates with function, make it feasible to use structure to predict function. However, current structure-based methods overlook the fact that individual residues may contribute differently to the protein's function and do not take into account the correlation between protein residues and their functions...

Bumblebees are crucial pollinators, providing essential ecosystem services and global food production. The success of pollination services relies on the interaction between sensory organs and the environment. The antenna functions as a versatile multi-sensory organ, pivotal in mediating chemosensory/olfactory information, and governs adaptive responses to environmental changes. Despite an increasing number of RNA-sequencing studies on insect antenna, comprehensive antennal transcriptome studies at the different life stages were not elucidated systematically...

The effects of global warming, population growth, and economic development are increasing the frequency of extreme weather events, such as drought. Among abiotic stresses, drought has the greatest impact on soil biological activity and crop yields. The rhizosphere microbiota, which represents a second gene pool for plants, may help alleviate the effects of drought on crops. In order to investigate the structure and diversity of the bacterial communities on drought stress, this study analyzed the differences in the bacterial communities by high-throughput sequencing and bioinformatical analyses in the rhizosphere of Arabidopsis thaliana under normal and drought conditions...

Numerous studies have been conducted to investigate the genomic characterization of bZIP genes and their involvement in the cellular response to endoplasmic reticulum (ER) stress. These studies have provided valuable insights into the coordinated cellular response to ER stress, which is mediated by bZIP transcription factors (TFs). However, a comprehensive and systematic investigations regarding the role of bZIP genes and their involvement in ER stress response in pak choi is currently lacking in the existing literature...

Gliomas are the most prevalent primary malignant tumors affecting the brain, with high recurrence and mortality rates. Accurate diagnoses and effective treatment challenges persist, emphasizing the need for identifying new biomarkers to guide clinical decisions. Long noncoding RNAs (lncRNAs) hold potential as diagnostic and therapeutic biomarkers in cancer. However, only a limited subset of lncRNAs in gliomas have been explored. Therefore, this study aims to identify lncRNA signatures applicable to patients with gliomas across all grades and explore their clinical significance and potential biological mechanisms...

The precise identification of splice sites is essential for unraveling the structure and function of genes, constituting a pivotal step in the gene annotation process. In this study, we developed a novel deep learning model, DRANetSplicer, that integrates residual learning and attention mechanisms for enhanced accuracy in capturing the intricate features of splice sites. We constructed multiple datasets using the most recent versions of genomic data from three different organisms, Oryza sativa japonica , Arabidopsis thaliana and Homo sapiens ...

MYCN amplification ( MNA ) and disruption of tumor suppressor microRNA (TSmiR) function are key drivers of poor outcomes in neuroblastoma (NB). While MYCN and TSmiRs regulate glucose metabolism, their role in de novo fatty acid synthesis (FAS) and unsaturated FAS (UFAS) remains poorly understood. Here, we show that FAS and UFAS (U/FAS) genes FASN , ELOVL6 , SCD , FADS2 , and FADS1 are upregulated in high-risk (HR) NB and that their expression is associated with lower overall survival. RNA-Seq analysis of human NB cell lines revealed parallel U/FAS gene expression patterns...

To systematically evaluate the effect of simulated long-term spaceflight composite stress (LSCS) in hippocampus and gain more insights into the transcriptomic landscape and molecular mechanism, we performed whole-transcriptome sequencing based on the control group (Ctrl) and the simulated long-term spaceflight composite stress group (LSCS) from six hippocampus of rats. Subsequently, differential expression analysis was performed on the Ctrl and LSCS groups, followed by enrichment analysis and functional interaction prediction analysis to investigate gene-regulatory circuits in LSCS...

Recent research has highlighted complex and close interaction between miRNAs, autophagy, and viral infection. In this study, we observed the autophagy status in CIK cells infected with GCRV at various time points. We found that GCRV consistently induced cellar autophagy from 0 h to 12 h post infection. Subsequently, we performed deep sequencing on CIK cells infected with GCRV at 0 h and 12 h respectively, identifying 38 DEMs and predicting 9,581 target genes. With the functional enrichment analyses of GO and KEGG, we identified 35 autophagy-related target genes of these DEMs, among which akt3 was pinpointed as the most central hub gene using module assay of the PPI network...

(1) BACKGROUND: Hashimoto's thyroiditis (HT) can cause angiogenesis in the thyroid gland. However, the molecular mechanism of endothelial cells and angiogenesis related genes (ARGs) has not been extensively studied in HT. (2) METHODS: The HRA001684, GSE29315 and GSE163203 datasets were included in this study. Using single-cell analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, machine learning algorithms and expression analysis for exploration. And receiver operator characteristic (ROC) curves was draw...

The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene-gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk...