Prediction of Protein Expression and Growth Rates by Supervised Machine Learning

Autor:	Simiao Zhao
Rok vydání:	2021
Předmět:	Computer science business.industry Applied Mathematics Supervised learning Process (computing) Machine learning computer.software_genre ENCODE DNA sequencing Data set chemistry.chemical_compound chemistry Artificial intelligence Transcription (software) business computer Encoder DNA
Zdroj:	Natural Science. 13:301-330
ISSN:	2150-4105 2150-4091
Popis:	The DNA sequences of an organism play an important influence on its transcription and translation process, thus affecting its protein production and growth rate. Due to the com-plexity of DNA, it was extremely difficult to predict the macroscopic characteristics of or-ganisms. However, with the rapid development of machine learning in recent years, it be-comes possible to use powerful machine learning algorithms to process and analyze biolog-ical data. Based on the synthetic DNA sequences of a specific microbe, E. coli, I designed a process to predict its protein production and growth rate. By observing the properties of a data set constructed by previous work, I chose to use supervised learning regressors with encoded DNA sequences as input features to perform the predictions. After comparing different encoders and algorithms, I selected three encoders to encode the DNA sequences as inputs and trained seven different regressors to predict the outputs. The hy-per-parameters are optimized for three regressors which have the best potential prediction performance. Finally, I successfully predicted the protein production and growth rates, with the best R2 score 0.55 and 0.77, respectively, by using encoders to catch the potential fea-tures from the DNA sequences.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::70df6705ad4ed4f4571ae7de8e8bf7c6 https://doi.org/10.4236/ns.2021.138025 Zobrazit plný text záznamu