High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution and are key signals that indicate viral cross-species infection and cause pandemics. As indicators for sensing the dynamic changes of viral genes, these higher order structures have been the focus of attention in the field of virology. However, the size of the viral gene network is usually huge, and searching these structures in the networks introduces unacceptable delay. To mitigate this issue, in this paper, we propose a simple-yet-effective model named HyperSearch based on deep learning to search cavities in a computable complex network for influenza virus genetics. Extensive experiments conducted on a public influenza virus dataset demonstrate the effectiveness of HyperSearch over other advanced deep-learning methods without any elaborated model crafting. Moreover, HyperSearch can finish the search works in minutes while 0-1 programming takes days. Since the proposed method is simple and easy to be transferred to other complex networks, HyperSearch has the potential to facilitate the monitoring of dynamic changes in viral genes and help humans keep up with the pace of virus mutations.

无人机（无人驾驶飞机）动态包围是一个具有巨大潜力的新兴领域。研究人员通常会从生物系统中获得灵感，要么是从宏观世界（如鱼类学校或鸟类羊群）或类似基因调节网络等微世界的灵感。但是，大多数群体控制算法都取决于集中控制，全球信息获取或相邻代理之间的通信。在这项工作中，我们提出了一种纯粹基于视觉的分布式群体控制方法，而没有任何直接通信，例如，群体的代理无人机可以生成一个陷入的模式，以完全基于其安装的全向视觉传感器包围无人机的逃脱目标。还设计了描述每种无人机行为模型的有限状态机器，以便一群无人机可以集体地搜索和捕获目标。我们在各种模拟和现实实验中验证了所提出方法的有效性和效率。

我们提出了一种乐观的基于模型的算法，Dubbed SMRL，用于通过指数族分布指定的转换模型，以D $参数指定，奖励是有界和已知的。SMRL使用得分匹配，一种无通量的密度估计技术，可以通过RIDGE回归有效地估计模型参数。在标准规律性假设下，SMRL实现$ \ tilde o（d \ sqrt {h ^ 3t}）$在线遗憾，其中$ h $是每一集的长度，$ t $是互动的总数（忽略多项式依赖结构尺度参数）。

Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and edges represent intermolecular interactions (inhibition, regulation, promotion, coexpression, etc.). In this work, a novel algorithm for the discovery of latent graph structures given experimental data is presented.

With the development of gene sequencing technology, an explosive growth of gene data has been witnessed. And the storage of gene data has become an important issue. Traditional gene data compression methods rely on general software like G-zip, which fails to utilize the interrelation of nucleotide sequence. Recently, many researchers begin to investigate deep learning based gene data compression method. In this paper, we propose a transformer-based gene compression method named GeneFormer. Specifically, we first introduce a modified transformer structure to fully explore the nucleotide sequence dependency. Then, we propose fixed-length parallel grouping to accelerate the decoding speed of our autoregressive model. Experimental results on real-world datasets show that our method saves 29.7% bit rate compared with the state-of-the-art method, and the decoding speed is significantly faster than all existing learning-based gene compression methods.

Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when data is costly to collect and/or noisy, e.g., in the case of gene expression profile data. In this paper, we present a reproducible method for inferring PBNs directly from real gene expression data measurements taken when the system was at a steady state. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. The proposed approach does not rely on reconstructing the state evolution of the network, which is computationally intractable for larger networks. We demonstrate the method on samples of real gene expression profiling data from a well-known study on metastatic melanoma. The pipeline is implemented using Python and we make it publicly available.

从基因表达数据中提取信息的广泛使用方法采用基因共表达网络的构建以及随后发现网络结构的算法的应用。特别是，一个共同的目标是基因簇的计算发现，通常称为模块。当应用新的基因表达数据集上时，可以使用基因本体学富集自动评估计算模块的质量，该方法可在计算的模块中测量基因本体论项的频率并评估其统计学上的可能性。在这项工作中，我们建议基于光谱网络理论数学中相对较新的开创性工作，提出了SGC的基因聚类的新型管道。 SGC由多个新型步骤组成，这些步骤能够以无监督的方式计算高度富集的模块。但是，与所有现有框架不同，它进一步结合了一个新的步骤，该步骤在半监督聚类方法中利用基因本体学信息，进一步提高了计算模块的质量。与已经众所周知的现有框架相比，我们表明SGC导致实际数据的富集更高。特别是，在12个实际基因表达数据集中，SGC的表现优于除1个。

基因调节网络是负责确定蛋白质和肽生产水平的生物生物体相互作用的网络。蛋白质是细胞工厂的工人，其生产定义了细胞及其开发的目标。已经进行了各种尝试来建模此类网络，以更好地了解这些生物系统，并利用了解它们的灵感来解决计算问题。在这项工作中，提出了一个针对基因调节网络的生物学上更现实的模型，该模型结合了细胞自动机和人工化学，以模拟称为转录因子和基因调节位点的调节蛋白之间的相互作用。这项工作的结果表明，复杂的动力学接近自然界中可以观察到的东西。在这里，对系统的初始状态对产生的动力学的影响进行了分析，这表明可以将这种可转化的模型针对产生所需的蛋白质动力学。

深度学习的可解释性被广泛用于评估医学成像模型的可靠性，并降低患者建议不准确的风险。对于超过人类绩效的模型，例如从显微镜图像中预测RNA结构，可解释的建模可以进一步用于发现高度非平凡的模式，而这些模式原本是人眼无法察觉的。我们表明，可解释性可以揭示癌组织的微观外观与其基因表达分析之间的联系。尽管从组织学图像中对所有基因进行详尽的分析仍然具有挑战性，但我们估计了癌症分子亚型，生存和治疗反应的众所周知的基因子集的表达值。我们的方法成功地从图像幻灯片中确定了有意义的信息，突出了高基因表达的热点。我们的方法可以帮助表征基因表达如何塑造组织形态，这可能对病理单位中的患者分层有益。该代码可在GitHub上找到。

昼夜节律的破坏是阿尔茨海默氏病（AD）患者的基本症状。人类脑中基因表达的完整昼夜节律编排及其与AD的固有关联仍然很大程度上是未知的。我们提出了一种新颖的综合方法，即Prime，以检测和分析在多个数据集中不合时宜的高维基因表达数据中的节奏振荡模式。为了证明Prime的实用性，首先，我们通过从小鼠肝脏中的时间课程表达数据集作为跨物种和跨器官验证来对其进行验证。然后，我们将其应用于研究来自19个对照和AD患者的19个人脑区域的未接收基因组基因表达中的振荡模式。我们的发现揭示了15对控制大脑区域中清晰，同步的振荡模式，而这些振荡模式要么消失或昏暗。值得注意的是，Prime在不需要样品的时间戳而发现昼夜节律的节奏模式。 Prime的代码以及在本文中复制数字的代码，可在https://github.com/xinxingwu-uk/prime上获得。