Multi-OMICS approaches in cancer biology: New era in cancer therapy

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Год журнала: 2024, Номер 1870(5), С. 167120 - 167120

Опубликована: Март 13, 2024

Язык: Английский

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Transforming Clinical Research: The Power of High-Throughput Omics Integration

Proteomes, Год журнала: 2024, Номер 12(3), С. 25 - 25

Опубликована: Сен. 6, 2024

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination current landscape high-throughput pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry microarray platforms highlights contribution to volume precision. In addition, this critical role bioinformatics tools statistical methods managing large datasets generated by these technologies. By integrating multi-omics data, researchers can gain holistic understanding systems, leading identification new biomarkers therapeutic targets, particularly complex diseases such as cancer. The also electronic health records (EHRs) potential for cloud computing big analytics improve storage, analysis sharing. Despite significant advances, there are still challenges complexity, technical limitations ethical issues. Future directions include development more sophisticated computational application advanced machine learning techniques, which addressing heterogeneity datasets. aims serve valuable resource practitioners, highlighting transformative advancing personalized medicine improving clinical outcomes.

Язык: Английский

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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

Biology, Год журнала: 2024, Номер 13(11), С. 848 - 848

Опубликована: Окт. 22, 2024

With the advent of high-throughput technologies, field omics has made significant strides in characterizing biological systems at various levels complexity. Transcriptomics, proteomics, and metabolomics are three most widely used each providing unique insights into different layers a system. However, analyzing data set separately may not provide comprehensive understanding subject under study. Therefore, integrating multi-omics become increasingly important bioinformatics research. In this article, we review strategies for transcriptomics, data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment interactome analysis. We discuss combined integration approaches, correlation-based strategies, machine learning techniques that utilize one or more types data. By presenting these methods, aim to researchers with better how integrate gain view system, facilitating identification complex patterns interactions might be missed by single-omics analyses.

Язык: Английский

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Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Human Genomics, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 31, 2025

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory cancers, diabetes, and mental health disorders pose a significant global challenge, accounting for the majority of fatalities disability-adjusted life years worldwide. These arise from complex interactions between genetic, behavioral, environmental factors, necessitating thorough understanding these dynamics to identify effective diagnostic strategies interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability explore interactions, several challenges remain. include inherent complexity heterogeneity multi-omic datasets, limitations analytical approaches, severe underrepresentation non-European genetic ancestries most omics which restricts generalizability findings exacerbates disparities. This scoping review evaluates landscape data related NCDs 2000 2024, focusing on advancements integration, translational applications, equity considerations. We highlight need standardized protocols, harmonized data-sharing policies, advanced approaches artificial intelligence/machine learning integrate study gene-environment interactions. also opportunities translating insights (GxE) research into precision medicine strategies. underscore potential advancing enhancing patient outcomes across diverse underserved populations, emphasizing fairness-centered strategic investments build local capacities underrepresented populations regions.

Язык: Английский

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Deep learning-based approaches for multi-omics data integration and analysis

BioData Mining, Год журнала: 2024, Номер 17(1)

Опубликована: Окт. 2, 2024

Язык: Английский

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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification

IEEE Access, Год журнала: 2025, Номер 13, С. 37724 - 37736

Опубликована: Янв. 1, 2025

Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of cancer disease process. Computational models based graph neural networks and attention-based architectures have demonstrated promising results for classification due their ability model complex relationships among biological entities. However, challenges related addressing high dimensionality complexity in multi-omics data, as well constructing structures that effectively capture interactions between nodes, remain active areas research. This study evaluates network (MO) integration graph-convolutional (GCN), graph-attention (GAT), graph-transformer (GTN). Differential gene expression LASSO (Least Absolute Shrinkage Selection Operator) regression are employed reducing feature selection; hence, developed referred LASSO-MOGCN, LASSO-MOGAT, LASSO-MOGTN. Graph constructed using sample correlation matrices protein-protein interaction investigated. Experimental validation is performed with a dataset 8,464 samples from 31 types normal tissue, comprising messenger-RNA, micro-RNA, DNA methylation data. The show outperformed trained single where LASSO-MOGAT achieved best overall performance, an accuracy 95.9%. findings also suggest correlation-based enhance models' identify shared cancer-specific signatures across patients comparison networks-based structures. code used this available link (https://github.com/FadiAlharbi2024/Graph_Based_Architecture.git).

Язык: Английский

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Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine

Biology, Год журнала: 2024, Номер 13(5), С. 338 - 338

Опубликована: Май 13, 2024

Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating intricate interactions among various components. We present a novel approach multi-omics data integration on knowledge graphs applied it to etiology classification task of 30 patients through integrative analysis DNA methylation mRNA, miRNA, circRNA. This has demonstrated promising performance as compared other existing single technology approaches.

Язык: Английский

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A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(12), С. 6422 - 6422

Опубликована: Июнь 11, 2024

The process of identification and management neurological disorder conditions faces challenges, prompting the investigation novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review identify significance genetics- molecular-pathway-based machine learning (ML) models treating conditions. According study's objectives, search strategies were developed extract research studies using digital libraries. We followed rigorous study selection criteria. A total 24 met inclusion criteria included review. classified based on disorders. highlighted multiple methodologies exceptional results findings underscore potential existing models, presenting personalized interventions individual's offer better-performing approaches that handle genetics molecular data generate effective outcomes. Moreover, discuss future directions emphasizing demand for generalizing real-world clinical settings. This contributes advancing knowledge field diagnosis

Язык: Английский

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Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Frontiers in Artificial Intelligence, Год журнала: 2024, Номер 7

Опубликована: Июль 3, 2024

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. proves valuable in guiding treatment decisions, optimizing resource allocation, interventions precision medicine. The wide range diseases, existence various variants within same disease, reliance on available data necessitate disease-specific computational survival predictors. widespread adoption artificial intelligence (AI) methods crafting predictors has undoubtedly revolutionized this field. However, ever-increasing demand for more sophisticated effective models necessitates continued creation innovative advancements. To catalyze these advancements, it is crucial bring existing knowledge insights into a centralized platform. paper hand thoroughly examines 23 review studies provides concise overview their scope limitations. Focusing comprehensive set 90 most recent across 44 diverse delves types that are used development This exhaustive analysis encompasses utilized modalities along with detailed subsets features, feature engineering methods, specific statistical, machine deep learning approaches have been employed. It also about sources, open-source predictors, frameworks.

Язык: Английский

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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification

Academia Biology, Год журнала: 2024, Номер 2(3)

Опубликована: Авг. 30, 2024

The application of machine learning methods to analyze changes in gene expression patterns has recently emerged as a powerful approach cancer research, enhancing our understanding the molecular mechanisms underpinning development and progression. Combining data with other types omics been reported by numerous works improve classification outcomes. Despite these advances, effectively integrating high-dimensional multi-omics capturing complex relationships across different biological layers remains challenging. This paper introduces LASSO-MOGAT (LASSO-Multi-Omics Gated ATtention), novel graph-based deep framework that integrates messenger RNA, microRNA, DNA methylation classify 31 types. Utilizing differential analysis LIMMA LASSO regression for feature selection, leveraging Graph Attention Networks (GATs) incorporate protein-protein interaction (PPI) networks, captures intricate within data. Experimental validation using five-fold cross-validation demonstrates method's precision, reliability, capacity providing comprehensive insights into mechanisms. computation attention coefficients edges graph proposed graph-attention architecture based on interactions proved beneficial identifying synergies classification.

Язык: Английский

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