Huminiecki Łukasz
Department of Molecular Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, 00-901 Warsaw, Poland.
Entropy (Basel). 2020 Aug 27;22(9):942. doi: 10.3390/e22090942.
The gene is a fundamental concept of genetics, which emerged with the Mendelian paradigm of heredity at the beginning of the 20th century. However, the concept has since diversified. Somewhat different narratives and models of the gene developed in several sub-disciplines of genetics, that is in classical genetics, population genetics, molecular genetics, genomics, and, recently, also, in systems genetics. Here, I ask how the diversity of the concept impacts data-integration and data-mining strategies for bioinformatics, genomics, statistical genetics, and data science. I also consider theoretical background of the concept of the gene in the ideas of empiricism and experimentalism, as well as reductionist and anti-reductionist narratives on the concept. Finally, a few strategies of analysis from published examples of data-mining projects are discussed. Moreover, the examples are re-interpreted in the light of the theoretical material. I argue that the choice of an optimal level of abstraction for the gene is vital for a successful genome analysis.
基因是遗传学的一个基本概念，它随着20世纪初孟德尔遗传范式的出现而产生。然而，从那时起这个概念就呈现出多样化。在遗传学的几个子学科中，即经典遗传学、群体遗传学、分子遗传学、基因组学以及最近的系统遗传学中，出现了一些稍有不同的关于基因的叙述和模型。在此，我探讨该概念的多样性如何影响生物信息学、基因组学、统计遗传学和数据科学中的数据整合与数据挖掘策略。我还会思考基因概念在经验主义和实验主义思想中的理论背景，以及关于该概念的还原论和反还原论叙述。最后，讨论了一些从已发表的数据挖掘项目示例中得出的分析策略。此外，根据理论材料对这些示例进行了重新解读。我认为为基因选择最佳抽象水平对于成功的基因组分析至关重要。
