整合式多态学和人工智能:系统生物学新范式
Shashi Kant1, Deepika2, Saheli Roy3
1Department of Biotechnology, School of Biotechnology and Biosciences, Brainware University, Kolkata, India.
Omics : a journal of integrative biology
|November 7, 2025
概括
多omics集成通过结合基因组,转录组,蛋白质组和代谢组数据来推进系统生物学. 这种方法阐明了复杂的生物网络,有助于疾病研究和精确医学.
科学领域:
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 文字转录学 (Transcriptomics) 是一个学科.
- 蛋白质组学是指蛋白质组学.
- 代谢学 代谢学 代谢学
背景情况:
- 高通量欧米克技术使全面的生物探索成为可能.
- 系统生物学越来越依赖于整合性的多omics战略.
- 多omics数据集成对于理解细胞功能和生物体表型至关重要.
研究的目的:
- 批判性地评估多学科整合的原则,方法和应用.
- 突出多组学在癌症生物学,微生物工程和合成生物学中的作用.
- 讨论多学科数据分析当前的局限性和未来的解决方案.
主要方法:
- 综合多学科整合原则和方法的审查.
- 计算工具的分析,包括数据集成,网络分析和机器学习.
- 对综合性奥米克斯可采取行动的发现进行案例研究分析.
主要成果:
- 多omics集成澄清了分子相互作用和疾病病理学.
- 确定用于精密医学和合成生物学的强大的生物标志物.
- 克服了诸如高维度和数据异质性等挑战.
结论:
- 多学科的整合对于破译复杂的生物系统至关重要.
- 计算工具和人工智能的进步正在提高解释性和预测能力.
- 未来的方向包括单细胞的omics和云平台,用于更广泛的应用.
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