Rapid advancements in high-throughput technologies have resulted in genome-scale time series datasets. Uncovering the temporal sequence of gene regulatory events, in the form of time-varying gene regulatory networks (GRNs), demands computationally fast, accurate, and scalable algorithms. The existing algorithms can be divided into two categories: ones that are time-intensive and hence unscalable; and others that impose structural constraints to become scalable. In this paper, a novel algorithm, namely 'an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators' (TGS), is proposed. TGS is time-efficient and does not impose any structural constraints. Moreover, it provides such flexibility and time-efficiency, without losing its accuracy. TGS consistently outperforms the state-of-the-art algorithms in true positive detection, on three benchmark synthetic datasets. However, TGS does not perform as well in false positive rejection. To mitigate this issue, TGS+ is proposed. TGS+ demonstrates competitive false positive rejection power, while maintaining the superior speed and true positive detection power of TGS. Nevertheless, the main memory requirements of both TGS variants grow exponentially with the number of genes, which they tackle by restricting the maximum number of regulators for each gene. Relaxing this restriction remains a challenge as the actual number of regulators is not known a priori.

中文翻译：

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时变基因调控网络的快速重建。

高通量技术的快速发展已导致基因组规模的时间序列数据集。以时变基因调控网络（GRN）的形式揭示基因调控事件的时间序列，需要计算快速，准确且可扩展的算法。现有的算法可以分为两类：一类是时间密集的并且因此不可扩展的算法；以及其他施加结构性约束以使其可扩展的约束。本文提出了一种新颖的算法，即“利用候选候选调节子重建时变基因调节网络的算法”（TGS）。TGS是省时的，并且没有任何结构上的限制。而且，它提供了这样的灵活性和时间效率，而又不损失其准确性。在三个基准合成数据集上，TGS在真正的阳性检测中始终优于最新的算法。但是，TGS在假阳性排除方面表现不佳。为了缓解此问题，建议使用TGS +。TGS +展示了具有竞争力的假阳性排斥能力，同时保持了TGS的卓越速度和真正的阳性检测能力。尽管如此，两个TGS变体的主要存储需求随基因数量呈指数增长，它们通过限制每个基因的最大调节子数量来解决。放宽这一限制仍然是一个挑战，因为先验人员并不知道实际的监管机构数量。TGS +展示了具有竞争力的假阳性排斥能力，同时保持了TGS的卓越速度和真正的阳性检测能力。尽管如此，两个TGS变体的主要存储需求随基因数量呈指数增长，它们通过限制每个基因的最大调节子数量来解决。放宽这一限制仍然是一个挑战，因为先验人员并不知道实际的监管机构数量。TGS +展示了具有竞争力的假阳性排斥能力，同时保持了TGS的卓越速度和真正的阳性检测能力。尽管如此，两个TGS变体的主要存储需求随基因数量的增长而成倍增长，它们通过限制每个基因的最大调节子数量来解决。放宽这一限制仍然是一个挑战，因为先验人员并不知道实际的监管机构数量。