The Mina & Everard Goodman Faculty of Life Sciences, Gonda Multidisciplinary Brain Research Center, Institute of Nanotechnology, Bar-Ilan University, Ramat Gan 5290002, Israel.
The Alexander Kofkin Faculty of Engineering, Gonda Multidisciplinary Brain Research Center, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel.
Int J Mol Sci. 2022 Mar 30;23(7):3811. doi: 10.3390/ijms23073811.
Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.
行为神经科学随着机器视觉和机器学习技术的出现经历了一场技术驱动的革命。这些技术进步促进了高分辨率、高通量的复杂行为的捕捉和分析。因此，行为神经科学正在成为一个数据丰富的领域。虽然行为研究人员使用先进的计算工具来分析由此产生的数据集，但仍在寻找稳健和标准化的分析工具。与此同时，基因组学领域也随着大量技术的出现而爆炸式增长，这些技术使大规模数据集的生成成为可能。这些基因组学数据的增长推动了强大的计算方法的出现，用于分析这些数据。在这里，我们讨论了一个大型行为数据集的组成，以及行为数据和基因组学数据之间的差异和相似之处。然后，我们给出了一些可能对行为分析有用的与基因组学相关的工具的例子，并讨论了在考虑这两个领域时可能出现的概念。
