在'omics数据中计算发现生物活性的挑战
Luis Pedro Coelho1,2, Célio Dias Santos-Júnior2,3, Cesar de la Fuente-Nunez4,5,6,7
1Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Woolloongabba, Queensland, Australia.
从基因组数据中发现新的生物活性是具有挑战性的,因为它们的尺寸很小. 本综述探讨了传统方法的局限性,并突出了机器学习方法用于 prokaryotes 中的发现.
科学领域:
- 生物技术是生物技术.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 体表现出具有生物技术潜力的多种生物活动.
- 奥米克斯数据为新型分子发现提供了机会,但的识别是复杂的.
- 传统的蛋白质发现方法在上失败,因为它们的尺寸很小.
研究的目的:
- 审查传统发现方法的局限性.
- 突出用于识别新型生物活性的替代机器学习方法.
- 专注于 prokaryotic 基因组和元基因组中的类发现.
主要方法:
- 审查传统的序列相似性和功能注释限制.
- 探索基于机器学习的方法,用于预测.
- 从短开放式读取框架 (smORF) 和蛋白质溶解中识别的挑战分析.
主要成果:
- 传统的方法在发现方面产生了高的假阳性.
- 机器学习为短序的功能注释提供了更有效的替代方案.
- Prokaryotic 基因组和元基因组是新开采的丰富来源.
结论:
- 新型机器学习方法对于克服生物活性发现的局限性至关重要.
- 有效地挖掘omics数据需要专门的生物信息学工具用于.
- 了解的起源 (smORFs与蛋白质分解) 有助于发现工作.
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