This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their ...
In “ART: A machine learning Automated Recommendation Tool for synthetic biology,” led by Radivojevic, the researchers presented the algorithm, which is tailored to the particularities of the synthetic biology field: small training data sets, the need to quantify uncertainty, and ...
However, the practice of machine learning requires statistical and mathematical expertise that is scarce, and highly competed for ref. 34. Here, we provide a tool that leverages machine learning for synthetic biology’s purposes: the Automated Recommendation Tool (ART, Fig. 1). ART combines the ...
Researchers at the University of Pittsburgh School of Medicine have combined synthetic biology with a machine-learning algorithm to create human liver organoids with blood- and bile-handling systems. When implanted into mice with failing livers, the lab-grown replacement livers extended life. The stud...
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of...
To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the ...
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data 来自 EBSCO 喜欢 0 阅读量: 135 作者:C Zak,MH Garcia 摘要: New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental...
Unsupervised learning can obtain a data-distributed representation of its own existence, which is irrelevant to a specific task. It yields the patterns themselves, that is, the general characteristics of the data set. The use of unsupervised learning is promising to create new synthetic biology ...
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Machine learning enables detection of complex patterns using computer science and statistics34. With the increasing availability of large-scale data-sets, machine learning has helped advance numerous fields including cancer detection, cell behavior prediction, genomic analysis, and drug discovery35,36,37,...