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.
|Journal||International Journal of Molecular Sciences|
|State||Published - 1 Apr 2022|
Bibliographical noteFunding Information:
This work was supported by the Israel Science Foundation Grants 174/19 and 384/14 granted to Galit Shohat-Ophir and by Israel Science Foundation Grants 2958/21 and 3363/21 granted to Shahar Alon.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- behavioral analysis
- large datasets