走向多领域的合成数据集成
Kumar Selvarajoo1,2,3, Sebastian Maurer-Stroh1,2
1Biomolecular Sequence to Function Division, BII, (A*STAR), Singapore, 138671, Republic of Singapore.
Briefings in bioinformatics
|May 7, 2024
概括
计算模型生成合成的生物数据,特别是来自多组的生物数据. 这些先进的方法为了解细胞过程提供了新的途径,并在多omics尺度上做出推断.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 越来越多地生成大规模的生物数据集,特别是多omics数据.
- 越来越多的人对计算模型和算法越来越感兴趣,用于跨科学领域的合成数据生成.
研究的目的:
- 讨论合成数据生成在生物应用中的当前趋势.
- 在这个领域探索过程驱动和数据驱动的方法.
- 突出这些方法的潜力,用于多omics规模的细胞推断.
主要方法:
- 综合数据计算建模的最新进展的审查.
- 在生物应用中分析过程驱动的方法.
- 检查生物数据生成的数据驱动方法.
主要成果:
- 确定将计算模型应用于生物数据的关键趋势.
- 讨论过程驱动和数据驱动策略之间的相互作用.
- 强调新型细胞推断的潜力.
结论:
- 合成数据生成是生物学中快速发展的一个领域.
- 讨论的方法可以显著提高多omics数据分析.
- 未来的应用对更深入的细胞理解充满希望.
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