پروژه های آماده مهندسی ژنتیک

این پروژه یک مقاله آماده با موضوع، ادغام داده‌های چند-اُمیکس برای تحلیل سلول‌های تک‌سلولی در ۲۱ صفحه ورد می باشد. در ادامه چکیده ای از این مقاله را خواهیم داشت:   Integration of Multi-Omics Data: Approaches for integrating scRNA-seq data with other omics data (e.g., scATAC-seq, scDNA-seq) to gain deeper insights into cellular function and regulation. Abstract The integration of multi-omics data has transformed biomedical research, enabling us to approach biological systems and diseases in a holistic way. In this review, we overview some state-of-the-art computational methodologies for integrating multi-omics data and highlight the key advances in deep learning (DL), transfer learning (TL) as well contrastive methods. All these methods have shown promising results for mitigating issues with data heterogeneity, noise and high-dimensional spaces especially in single-cell frameworks like scRNA-seq or even scATAC-seq. Through a synthesis of new research, the review underscores just how powerful these techniques have become at unearthing details about the wormy molecular machinations and control networks that keep our cells ticking--or drive them off course. This is underlined by the theoretic implications presented above and strengthen in increasing comprehensiveness multi-omics integration of gene regulation, cellular states as well systems biology. From a research perspective, these advances have significant impacts on the design of experiments as well data analysis and ultimately bridge to clinical applications in precision medicine. Nevertheless, still face challenges such as interpretability of models, standardization in protocols or computational tools availability. Review closes with a sentence: “While much has been accomplished, more innovation is needed to bring multi-omics integration into full operation for both research and clinical applications. Key Words: Multi-Omics Integration, Deep Learning, Methods Single-Cell RNA Sequencing (scRNA-seq), Precision Medicine, Data Heterogeneity.