Abstract
This chapter is intended to outline methods and applications for machine learning (ML) in the context of protein engineering. The content of this chapter is geared toward the biologist’s perspective of ML with an emphasis on experimental design for optimized data collection. After a brief introduction, the subsequent sections of this chapter will be dedicated to following a schema to design and implement ML for problems involving protein engineering. The steps below offer a guide to this schema and the organization of this chapter in the context of protein engineering.
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I.
Formulate a question that ML tools can answer
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II.
Design an experiment to collect data
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III.
Curate the dataset
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IV.
Choose and train a model
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V.
Interpret results
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Marques, A.D. (2022). Machine Learning for Protein Engineering. In: Roy, S.S., Taguchi, YH. (eds) Handbook of Machine Learning Applications for Genomics. Studies in Big Data, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-16-9158-4_2
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