Gene regulatory network inference from sparsely sampled noisy data
Gene regulatory network inference from sparsely sampled noisy data
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DOI：
10.1038/s41467-020-17217-1
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发表时间：
2020-07-13
影响因子：
16.6
通讯作者：
Goncalves, Jorge
中科院分区：
文献类型：
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作者：
Aalto, Atte;Viitasaari, Lauri;Goncalves, Jorge
生物系统的复杂性被编码在基因调控网络中。解开这个错综复杂的网络是理解生命机制并最终开发出治疗和治愈疾病的有效疗法的基本步骤。推断基因调控网络的主要障碍是缺乏数据。虽然时间序列数据如今已广泛使用，但它们通常充满噪声、采样频率低且样本总数较少。本文开发了一种称为 BINGO 的方法来专门处理这些问题。以覆盖许多不同基因调控网络的真实和模拟时间序列数据为基准，BINGO 明显且始终优于最先进的方法。 BINGO 的新颖之处在于其非参数方法，其特点是对连续基因表达谱进行统计采样。 BINGO 的卓越性能和易用性（即使是非专业人士也能轻松使用）使任何研究人员都可以进行基因调控网络推断，帮助破译生命的复杂机制。
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Bench-marked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO's superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.