Challenges and Prospects of Machine Learning applied to Nucleic Acid Origami

Machine learning and computational algorithms used to see patterns within collected data are becoming increasingly popular and valuable across many intersecting fields of study. For biological fields there needs to be cooperation between scientists working in both computer science and biological fields to close the gap between traditional lab experiments data collection and sharing towards the prospect of more modern data curation methods that would be invaluable for the data mining of shared stored experimental data. Challenges include the representation of experimental data, the design and focus of experiments from their outset and when looking at comparability of experiments across papers with different goals.