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arXiv 2408.07064q-bio.MNphysics.bio-ph
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论网络及其应用：基因调控网络的稳定性与基于Autoencoder（自编码器）的基因功能预测

On Networks and their Applications: Stability of Gene Regulatory Networks and Gene Function Prediction using Autoencoders

Hamza Coban

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AI总结：

该研究证明嵌套canalizing函数是最低灵敏度布尔函数，为基因调控系统处于混沌边缘提供定量依据，同时提出半监督自编码器的功能特异性基因发现工具，还发现生物可信学习算法的动力学具有通用爱因斯坦关系。

摘要：

我们证明，对于任意活性比，嵌套 canalizing 函数（嵌套 canalizing function）都是灵敏度最低的布尔函数，同时我们确定了这一边界的函数形式，该边界具有非平凡的分形结构。我们进一步观察到，在已知生物网络（提交至Cell Collective数据库的网络）中发现的大多数基因调控函数都位于最低灵敏度线上，而矛盾的是，这条线很大程度上仍处于不稳定区域。有观点认为，进化过程中基因调控对嵌套 canalizing 函数的偏好（例如为了更高的鲁棒性）以及基因活性的弹性，足以让这类系统集中在“混沌边缘”附近，我们的结果为这一论点提供了定量基础。\n由于部分基因的功能尚未被发现，基因调控网络的原始结构是未知的。大多数基因功能发现方法都采用无监督聚类或分类方法，这些方法会发现并利用基因表达谱中的模式。然而，该领域的现有知识来源于多种多样的渠道。因此，将这些专门知识整合到新的基因功能预测中，有望提升这类预测的效果。我们在此提出一种功能特异性的新基因发现工具，它采用半监督autoencoder（自编码器）。因此，我们的方法能够满足通常仅专注于特定功能领域的现代研究者的需求。最后，我们研究了非正统学习方法（例如生物可信学习算法）的动力学特性，发现它们表现出一种通用形式的爱因斯坦关系（Einstein relation）。

英文摘要：

We prove that nested canalizing functions are the minimum-sensitivity Boolean functions for any activity ratio and we determine the functional form of this boundary which has a nontrivial fractal structure. We further observe that the majority of the gene regulatory functions found in known biological networks (submitted to the Cell Collective database) lie on the line of minimum sensitivity which paradoxically remains largely in the unstable regime. Our results provide a quantitative basis for the argument that an evolutionary preference for nested canalizing functions in gene regulation (e.g., for higher robustness) and for elasticity of gene activity are sufficient for concentration of such systems near the "edge of chaos." The original structure of gene regulatory networks is unknown due to the undiscovered functions of some genes. Most gene function discovery approaches make use of unsupervised clustering or classification methods that discover and exploit patterns in gene expression profiles. However, existing knowledge in the field derives from multiple and diverse sources. Incorporating this know-how for novel gene function prediction can, therefore, be expected to improve such predictions. We here propose a function-specific novel gene discovery tool that uses a semi-supervised autoencoder. Our method is thus able to address the needs of a modern researcher whose expertise is typically confined to a specific functional domain. Lastly, the dynamics of unorthodox learning approaches like biologically plausible learning algorithms are investigated and found to exhibit a general form of Einstein relation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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