ReviewEssays in biochemistry2016
Synthetic biology routes to bio-artificial intelligence.
Review in Essays in biochemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations.CPT: pharmacometrics & systems pharmacology · 2017Trial
- Artificial Intelligence in Genetics.Cureus · 2024Review
- The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks.Frontiers in bioengineering and biotechnology · 2024Review
- An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation.Sensors (Basel, Switzerland) · 2022Article
- Programming Molecular Systems To Emulate a Learning Spiking Neuron.ACS synthetic biology · 2022Article
- DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron.ACS synthetic biology · 2022Article
- Polyphosphate in Chronic Wound Healing: Restoration of Impaired Metabolic Energy State.Progress in molecular and subcellular biology · 2022Article
- Inorganic Polyphosphate: Coacervate Formation and Functional Significance in Nanomedical Applications.International journal of nanomedicine · 2022Review
- Harnessing biocompatible chemistry for developing improved and novel microbial cell factories.Microbial biotechnology · 2020Review
- Multi-cellular engineered living systems: building a community around responsible research on emergence.Biofabrication · 2019Article
- Synthetic biology advances and applications in the biotechnology industry: a perspective.Journal of industrial microbiology & biotechnology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular 'teachers' and 'students' is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI).
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.