Systems Biology Group, Life Science Research Unit, University of Luxembourg, 6, avenue du Swing, Belval, 4367, Luxembourg.
Experimental Dermatology, Department of Dermatology, Dresden University of Technology, Fetscherstraße 105, Dresden, 01307, Germany.
Theor Biol Med Model. 2020 May 14;17(1):8. doi: 10.1186/s12976-020-00126-7.
Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research.
基因、蛋白质或细胞相互影响，进而产生模式，实验生物学和医学可以越来越清晰地观察到这些模式。因此，统计学和生物信息学的描述性方法可以提高和优化我们的认知。然而，考虑到生物元素之间的相互连接性，有望更深入、更连贯地理解黑色素瘤。例如，基于整合网络的工具和有充分依据的归纳计算研究揭示了疾病机制、对患者进行分层，并支持治疗个体化。本文综述了统计学之外的不同建模技术，展示了不同策略如何与相应的医学生物学保持一致，并确定了计算黑色素瘤研究的新的可能领域。
