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Motivation As the most abundant mammalian mRNA methylation, N 6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. Results To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. Availability and implementation The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR) is one of the most common serious complications in diabetic patients, and early screening and diagnosis are essential to prevent visual impairment. With the rapid development of deep learning technology, network models based on attention mechanisms have shown significant advantages in medical image analysis, which can improve the accuracy and efficiency of screening., Aim: To evaluate the efficacy of an attention mechanism-based deep learning network model in screening for DR in natural and diabetic populations, as well as in screening with unilateral and bilateral fundus photography., Methods: From January 2023 to June 2024, a stratified multistage cluster sampling method was adopted to select a representative sample of permanent residents aged 18-70 years from our hospital. A total of 948 fundus images from 474 participants were included in the "deep learning model" system for scoring. The fundus images were graded via the early treatment of DR [Early Treatment Diabetic Retinopathy Study (ETDRS)] scoring system as the gold standard for the diagnosis of DR. With "DR to be referred (ETDRS > 31)" as the reference variable, a receiver operating characteristic curve was drawn to evaluate the area under the curve (AUC), sensitivity and specificity of the "deep learning model" to determine the screening efficiency of the system., Results: For each subject, in the natural population, the AUC of using the "deep learning model system" to screen "DR-requiring referral" was 0.941, and the sensitivity and specificity were 98.15% and 90.08%, respectively. The sensitivity and specificity of two-directional fundus photography were 100% and 86.91%, respectively. In the diabetic population, the AUC, sensitivity and specificity were 0.901, 98.08% and 82.10%, respectively, when "wise eye sugar net" unilateral fundus photography was used to screen for "DR-requiring referrals"., Conclusion: In both the natural population and the diabetic population, the deep learning model system has shown high sensitivity and specificity and can be used as an auxiliary means of DR screening., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)