Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes DOI Creative Commons
Jeong-Woon Park, Je‐Keun Rhee

Biology, Journal Year: 2024, Volume and Issue: 13(10), P. 799 - 799

Published: Oct. 7, 2024

Breast cancer is a heterogeneous disease composed of various biologically distinct subtypes, each characterized by unique molecular features. Its formation and progression involve complex, multistep process that includes the accumulation numerous genetic epigenetic alterations. Although integrating RNA-seq transcriptome data with ATAC-seq information provides more comprehensive understanding gene regulation its impact across different conditions, no classification model has yet been developed for breast intrinsic subtypes based on such integrative analyses. In this study, we employed machine learning algorithms to predict through analysis data. We identified 10 signature genes (

Language: Английский

Mechanisms and technologies in cancer epigenetics DOI Creative Commons
Zaki A. Sherif, Olorunseun O. Ogunwobi, Habtom W. Ressom

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 7, 2025

Cancer's epigenetic landscape, a labyrinthine tapestry of molecular modifications, has long captivated researchers with its profound influence on gene expression and cellular fate. This review discusses the intricate mechanisms underlying cancer epigenetics, unraveling complex interplay between DNA methylation, histone chromatin remodeling, non-coding RNAs. We navigate through tumultuous seas dysregulation, exploring how these processes conspire to silence tumor suppressors unleash oncogenic potential. The narrative pivots cutting-edge technologies, revolutionizing our ability decode epigenome. From granular insights single-cell epigenomics holistic view offered by multi-omics approaches, we examine tools are reshaping understanding heterogeneity evolution. also highlights emerging techniques, such as spatial long-read sequencing, which promise unveil hidden dimensions regulation. Finally, probed transformative potential CRISPR-based epigenome editing computational analysis transmute raw data into biological insights. study seeks synthesize comprehensive yet nuanced contemporary landscape future directions research.

Language: Английский

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Multimodal data integration in early-stage breast cancer DOI Open Access
Arnau Llinas-Bertran,

Maria Butjosa-Espín,

Vittoria Barberi

et al.

The Breast, Journal Year: 2025, Volume and Issue: unknown, P. 103892 - 103892

Published: Jan. 1, 2025

Language: Английский

Citations

0
The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer DOI Open Access
Vanesa Garcı́a,

María Elena Gómez del Pulgar,

Heidy M Guamán

et al.

Extracellular Vesicles and Circulating Nucleic Acids, Journal Year: 2025, Volume and Issue: 6(1), P. 128 - 40

Published: Feb. 28, 2025

Artificial intelligence (AI) is revolutionizing scientific research by facilitating a paradigm shift in data analysis and discovery. This transformation characterized fundamental change methods concepts due to AI’s ability process vast datasets with unprecedented speed accuracy. In breast cancer research, AI aids early detection, prognosis, personalized treatment strategies. Liquid biopsy, noninvasive tool for detecting circulating tumor traits, could ideally benefit from analytical capabilities, enhancing the detection of minimal residual disease improving monitoring. Extracellular vesicles (EVs), which are key elements cell communication progression, be analyzed identify disease-specific biomarkers. combined EV promises an enhancement diagnosis precision, aiding Studies show that can differentiate types predict drug efficacy, exemplifying its potential medicine. Overall, integration biomedical clinical practice significant changes advancements diagnostics, medicine-based approaches, our understanding complex diseases like cancer.

Language: Английский

Citations

0
An Attention‐Aware Multi‐Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia DOI Creative Commons

Md. Selim Reza,

Chuan Qiu, Lin Xu

et al.

Journal of Cachexia Sarcopenia and Muscle, Journal Year: 2025, Volume and Issue: 16(2)

Published: March 5, 2025

Language: Английский

Citations

0
A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction DOI Creative Commons
Rui Miao,

Bing-Jie Zhong,

Xin-Yue Mei

et al.

Frontiers in Genetics, Journal Year: 2025, Volume and Issue: 16

Published: March 21, 2025

Motivation Predicting the response of cell lines to characteristic drugs based on multi-omics gene information has become core problem precision oncology. At present, drug prediction using data faces following three main challenges: first, how design a probe feature extraction model with biological interpretation and high performance; second, develop weighting modules for reasonably fusing genetic different lengths noise conditions; third, construct deep learning models that can handle small sample sizes while minimizing risk possible overfitting. Results We propose an innovative (NMDP). First, NMDP introduces interpretable semi-supervised weighted SPCA module solve in data. Next, we fusion framework similarity networks, bimodal tests, variance information, which solves enables focus more relevant genomic Finally, combine one-dimensional convolution method Kolmogorov–Arnold networks (KANs) predict response. conduct five sets real experiments compare against seven advanced methods. The results show achieves best performance, sensitivity specificity reaching 0.92 0.93, respectively—an improvement 11%–57% compared other models. Bio-enrichment strongly support its ability identify potential targets activity prediction.

Language: Английский

Citations

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Network-based multi-omics integrative analysis methods in drug discovery: a systematic review DOI Creative Commons
Wei Jiang, Weicai Ye, Xiao-Ming Tan

et al.

BioData Mining, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 28, 2025

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate data, systematic evaluation and comparison these remain challenging. This review aims analyze approaches for evaluate their applications in We conducted a comprehensive literature (2015-2024) on discovery, categorized into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, inference models. also discussed the three scenario including target identification, response prediction, repurposing, finally evaluated performance by highlighting advantages limitations specific applications. shown promise challenges computational scalability, integration, biological interpretation. Future developments should focus incorporating temporal spatial dynamics, improving model interpretability, establishing standardized frameworks.

Language: Английский

Citations

0
Adaptive-weighted federated graph convolutional networks with multi-sensor data fusion for drug response prediction DOI

Yu Hui,

Qingyong Wang, Xiaobo Zhou

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103147 - 103147

Published: April 1, 2025

Language: Английский

Citations

0
Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges DOI Creative Commons
Yajun Mao,

Dangang Shangguan,

Qi Huang

et al.

Molecular Cancer, Journal Year: 2025, Volume and Issue: 24(1)

Published: April 23, 2025

Language: Английский

Citations

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Application of AI in detection of breast cancer with laboratory results monitoring DOI Creative Commons

Sabina Prevljak,

Amar Kustura, Berina Hasanefendić

et al.

Bioengineering Studies, Journal Year: 2024, Volume and Issue: 5(1), P. 1 - 14

Published: July 30, 2024

Breast cancer is one of the most common types among women worldwide, therefore an early and precise process diagnostics plays important role in improving prognosis outcome treatment. The application artificial intelligence (AI) allows faster more analysis medical imaging, which contributes to detection tumors lowers number false-negative results. This review article analyzed 60 scientific papers using recent findings about this topic, searched for AI implementation breast research how may improve overall survival outcomes patients.

Language: Английский

Citations

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Cell morphology and gene expression: tracking changes and complementarity across time and cell lines DOI Creative Commons

Vanille Lejal,

David Rouquié, Olivier Taboureau

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Summary Effective drug discovery relies on combining target knowledge with functional assays and multi-omics data to understand chemicals’ molecular actions. However, the relationship between cell morphology gene expression over time across lines remains unclear. To explore this, we analyzed Cell Painting L1000 for 106 compounds three from osteoblast, lung, breast tumors (U2OS, A549, MCF7) at points (6h, 24h, 48h) using a 10µM concentration. We found significant line effects in data, less pronounced transcriptomics. Using Weighted Gene Co-expression Network Analysis (WGCNA) enrichment analysis, identified connections deregulation chemicals similar biological (e.g., HDAC CDK inhibitors). These findings suggest that while shows distinct patterns, both technologies offer complementary insights into compound-induced cellular changes, enhancing chemical risk assessment.

Language: Английский

Citations

0