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Regulation of gene expression through multiple epigenetic components is a highly combinatorial process. Alterations in any of these layers, as is commonly found in cancer diseases, can lead to a cascade of downstream effects on tumor suppressor or oncogenes. Hence, deciphering the effects of epigenetic alterations on regulatory elements requires innovative computational approaches that can benefit from the huge amounts of epigenomic datasets that are available from multiple consortia, such as Roadmap or BluePrint. We developed a software tool named IRENE (Integrative Ranking of Epigenetic Network of Enhancers), which performs quantitative analyses on differential epigenetic modifications through an integrated, network-based approach. The method takes into account the additive effect of alterations on multiple regulatory elements of a gene. Applying this tool to well-characterized test cases, it successfully found many known cancer genes from publicly available cancer epigenome datasets.
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http://dx.doi.org/10.3389/fgene.2021.664654 | DOI Listing |
PLoS One
September 2025
Department of Maths and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, The Democratic Republic of the Congo.
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions.
View Article and Find Full Text PDFPLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPest Manag Sci
September 2025
AgResearch Ltd, Tuhiraki, Lincoln, New Zealand.
Background: Conventional weed risk assessments (WRAs) are time-consuming and often constrained by species-specific data gaps. We present a validated, algorithmic alternative, the model, that integrates climatic suitability ( ), weed-related publication frequency (P) and global occurrence data ( ), using publicly available databases and artificial intelligence (AI)-assisted text screening with a large language model (LLM).
Results: The model was tested against independent weed hazard classifications for New Zealand and California.
Medicine (Baltimore)
September 2025
The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Background: Multiple non-pharmacological and nonsurgical interventions have demonstrated efficacy in improving abdominal obesity. However, the optimal intervention remains uncertain. This study aimed to assess the relative effectiveness and safety of these interventions in reducing waist circumference, waist-to-hip ratio, waist-to-height ratio (WHtR), body mass index (BMI), and body weight among adults with abdominal obesity.
View Article and Find Full Text PDFFront Bioeng Biotechnol
August 2025
Department of Gastroenterology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Introduction: Colon cancer ranks among the most prevalent and lethal cancers globally, emphasizing the urgent need for accurate and early diagnostic tools. Recent advances in deep learning have shown promise in medical image analysis, offering potential improvements in detection accuracy and efficiency.
Methods: This study proposes a novel approach for classifying colon tissue images as normal or cancerous using Detectron2, a deep learning framework known for its superior object detection and segmentation capabilities.