目的:基因调控网络(GRN)推断是生物学和医学中的一项基本任务,因为它可以更深入地了解生物中基因表达的复杂机制。该生物信息学问题已通过多种计算方法在文献中得到解决。为从表达数据推断而开发的技术采用了贝叶斯网络,常微分方程(ODE),机器学习,信息论测度和神经网络,在其他人中。实现方式的多样性及其各自的定制化,导致了许多工具的出现以及由此衍生的多个专门领域,被理解为具有特定特征的网络子集,这些特征在先验检测方面具有挑战性。在为特定数据集选择最合适的技术时,这种专业化引入了显着的不确定性。这个提议,名为MO-GENECI,建立在先前提议GENECI的基本思想之上,并优化了不同推理技术之间的共识,通过以各种目标函数为指导的精心完善的多目标进化算法,与手头的生物环境有关。
方法:MO-GENECI已在广泛而多样的学术基准上进行了测试,该基准包括来自多个来源和规模的106个基因调控网络。MO-GENECI的评估将其性能与使用关键指标(AUROC和AUPR)进行基因调控网络推断的单个技术进行了比较。弗里德曼的统计排名提供了有序的分类,然后进行非参数Holm检验以确定统计学意义。
结果:MO-GENECI\的Pareto前沿近似有助于根据通用输入数据特征轻松选择合适的解决方案。在所有统计测试中,最佳解决方案始终是赢家,在很多情况下,中位数精度解决方案与获胜者相比无统计学差异.
结论:MO-GENECI不仅证明了比单个技术获得更准确的结果,但由于其灵活性和适应性,也克服了与初始选择相关的不确定性。显示了为每种情况智能地选择最合适的技术。源代码托管在GitHub的公共存储库中,并获得MIT许可:https://github.com/AdrianSeguraOrtiz/MO-GENECI。此外,为了方便其安装和使用,与此实现相关的软件已封装在PyPI:https://pypi.org/project/geneci/上的Python包中。
OBJECTIVE: Gene Regulatory Network (GRN)
inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different
inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.
METHODS: MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network
inference. Friedman\'s statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.
RESULTS: MO-GENECI\'s Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.
CONCLUSIONS: MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.