ArticleMethods in molecular biology (Clifton, N.J.)2023
Synthetic Biology Meets Machine Learning.
Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Rethinking natural product discovery to unblock the antibiotic pipeline.Future microbiology · 2025Article
- Heterologous production of caffeic acid in microbial hosts: current status and perspectives.Frontiers in microbiology · 2025Review
- Engineering biology and automation-Replicability as a design principle.Engineering biology · 2024Review
- High-throughput prediction of enzyme promiscuity based on substrate-product pairs.Briefings in bioinformatics · 2024Article
- An engineering biology approach to automated workflow and biodesign.Synthetic biology (Oxford, England) · 2024Review
- Applications of artificial intelligence and machine learning in dynamic pathway engineering.Biochemical Society transactions · 2023Review
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
Indexed as
Identifiers
36227537What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.