Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs).
View Article and Find Full Text PDFBackground: Different from typical primary central nervous system lymphoma (PCNSL), early-stage atypical PCNSL usually presents as patchy signal abnormalities without evident mass effect or significant contrast enhancement and is prone to confusion with low-grade glioma (LGG). This study aims to develop a magnetic resonance imaging (MRI)-based radiomics model to differentiate early-stage atypical PCNSL from LGG.
Methods: Two cohorts consisting of early-stage atypical PCNSL patients, as well as LGG patients with similar radiological manifestations, were retrospectively recruited from West China Hospital of Sichuan University (PCNSL = 75; LGG = 138) and Chengdu Shangjin Nanfu Hospital (PCNSL = 35; LGG = 72) to serve as the training set and external validation set, respectively.
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions.
View Article and Find Full Text PDFN-methyladenosine (mA) plays a crucial role in enriching RNA functional and genetic information, and the identification of mA modification sites is therefore an important task to promote the understanding of RNA epigenetics. In the identification process, current studies are mainly concentrated on capturing the short-range dependencies between adjacent nucleotides in RNA sequences, while ignoring the impact of long-range dependencies between non-adjacent nucleotides for learning high-quality representation of RNA sequences. In this work, we propose an end-to-end prediction model, called mASLD, to improve the identification accuracy of mA modification sites by capturing the short-range and long-range dependencies of nucleotides.
View Article and Find Full Text PDFModeling molecular activity and quantitative structure-activity relationships of chemical compounds is critical in drug design. Graph neural networks, which utilize molecular structures as frames, have shown success in assessing the biological activity of chemical compounds, guiding the selection and optimization of candidates for further development. However, current models often overlook activity cliffs (ACs)-cases where structurally similar molecules exhibit different bioactivities-due to latent spaces primarily optimized for structural features.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
March 2025
Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding effectiveness of graph neural networks (GNNs) has garnered significant interest in the field of DDI prediction. Consequently, there has been a notable surge in the development of network-based computational approaches for predicting DDIs.
View Article and Find Full Text PDFBiomed Pharmacother
July 2024
Drug resistance in hepatocellular carcinoma has posed significant obstacles to effective treatment. Recent evidence indicates that, in addition to traditional gene mutations, epigenetic recoding plays a crucial role in HCC drug resistance. Unlike irreversible gene mutations, epigenetic changes are reversible, offering a promising avenue for preventing and overcoming drug resistance in liver cancer.
View Article and Find Full Text PDFObjective: Unresectable hepatocellular carcinoma (uHCC) continues to pose effective treatment options. The objective of this study was to assess the efficacy and safety of combining low-dose cyclophosphamide with lenvatinib, pembrolizumab and transarterial chemoembolization (TACE) for the treatment of uHCC.
Methods: From February 2022 to November 2023, a total of 40 patients diagnosed with uHCC were enrolled in this small-dose, single-center, single-arm, prospective study.
IEEE J Biomed Health Inform
July 2024
As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level.
View Article and Find Full Text PDFBackground: Primary liver cancer is a malignant tumor with a high recurrence rate that significantly affects patient prognosis. Postoperative adjuvant external radiation therapy (RT) has been shown to effectively prevent recurrence after liver cancer resection. However, there are multiple RT techniques available, and the differential effects of these techniques in preventing postoperative liver cancer recurrence require further investigation.
View Article and Find Full Text PDFAs a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites.
View Article and Find Full Text PDFInfection of pigs with the pseudorabies virus (PRV) causes significant economic losses in the pig industry. Immunization with live vaccines is a crucial aspect in the prevention of pseudorabies in swine. The TK/gE/gI/11k/28k deleted pseudorabies vaccine is a promising alternative for the eradication of epidemic pseudorabies mutant strains.
View Article and Find Full Text PDFObjective: H3 G34-mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase-wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them.
Methods: Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study.
Background: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters.
Results: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks.
The heart is the main organ of the circulatory system and requires fatty acids to maintain its activity. Stress is a contributor to aggravating cardiovascular diseases and even death, and exacerbates the abnormal lipid metabolism. The cardiac metabolism may be disturbed by stress.
View Article and Find Full Text PDFLong non-coding RNAs have been reported to play a crucial role in tumor progression in hepatocellular carcinoma (HCC). Lnc-ZEB2-19 has been validated to be deficiently expressed in HCC. However, the capabilities and underlying mechanisms of lnc-ZEB2-19 remain uncertain.
View Article and Find Full Text PDFRationale And Objectives: The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs).
Materials And Methods: We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions.