关于组装理论及其分子生物特征分类方法的突出局限性。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Abicumaran Uthamacumaran, Felipe S Abrahão, Narsis A Kiani, Hector Zenil
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引用次数: 0

摘要

我们证明,装配理论(AT)所依据的装配路径方法是一种被流行的统计压缩算法广泛使用的编码方案。我们的研究表明,在所有情况下(合成或自然),AT 的性能与其他简单编码方案相似,但与基于算法概率的系统相关指数相比,AT 的性能较低,后者不仅考虑了统计重复,还考虑了其他可计算模式的可能性。我们的研究结果表明,与现有方法(包括传统的统计方法)相比,组装指数并没有实质性的改进,这也意味着采用这些方法来区分生命化合物和非生命化合物的研究之前已有报道。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures.

On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures.

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account statistical repetitions but also the likelihood of other computable patterns. Our results imply that the assembly index does not offer substantial improvements over existing methods, including traditional statistical ones, and imply that the separation between living and non-living compounds following these methods has been reported before.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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