Genome engineering is undergoing unprecedented development and is now becoming widely available. To ensure responsible biotechnology innovation and to reduce misuse of engineered DNA sequences, it is vital to develop tools to identify the lab-of-origin of engineered plasmids. Genetic engineering attribution (GEA), the ability to make sequence-lab associations, would support forensic experts in this process. Here, we propose a method, based on metric learning, that ranks the most likely labs-of-origin whilst simultaneously generating embeddings for plasmid sequences and labs. These embeddings can be used to perform various downstream tasks, such as clustering DNA sequences and labs, as well as using them as features in machine learning models.

Genome engineering is undergoing unprecedented development and is now becoming widely available. To ensure responsible biotechnology innovation and to reduce misuse of engineered DNA sequences, it is vital to develop tools to identify the lab-of-origin of engineered plasmids. Genetic engineering attribution (GEA), the ability to make sequence-lab associations, would support forensic experts in this process. Here, we propose a method, based on metric learning, that ranks the most likely labs-of-origin whilst simultaneously generating embeddings for plasmid sequences and labs. These embeddings can be used to perform various downstream tasks, such as clustering DNA sequences and labs, as well as using them as features in machine learning models. Our approach employs a circular shift augmentation approach and is able to correctly rank the lab-of-origin $90\%$ of the time within its top 10 predictions - outperforming all current state-of-the-art approaches. We also demonstrate that we can perform few-shot-learning and obtain $76\%$ top-10 accuracy using only $10\%$ of the sequences. This means, we outperform the previous CNN approach using only one-tenth of the data. We also demonstrate that we are able to extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model's outputs.

Efforts to harness the power of livings cells have just taken a huge stride forward, thanks to a new method which could revolutionise synthetic biology. Scientists have been able to'hack' a living cell using a simple computer programming language, allowing them to tweak the functions of cells. The findings lay the groundwork for anyone to easily write in new functions to living cells, enabling them to carry out any task. Scientists at MIT have'hacked' a living cell using a simple computer programming language to create synthetic DNA (illustrated), allowing them to tweak the functions of bacteria. By engineering DNA'circuits' - which mimic electronic circuits - bioengineers in the US were able to rewrite the code of bacteria, changing their functions to environmental cues.

References 67 October 27, 1977 1 Introduction This application addresses the continuation of research on the applications of artificial intelligence (Al) (1) to experimental molecular genetics. It is an extension of a longstanding effort to cultivate attention to ongoing laboratory research as a domain of explorations in artificial intelligence. Our major effort in this field had been in the DENDRNL project, with analytical organic chemistry as the object discipline.

The Experimental Steps in Detaii... 5 IV. Review of Knowledge Used in This Experiment 10 IV51 Proposing the Experiment..... 10 IV.2 Establishing Some Subgoals.

For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as it is usually accepted, biologically inspired . On the contrary, I have drawn my inspirations from a pure mathematical theory, the Kolmogorov s complexity theory. The results of my work have been already published elsewhere. So the objective of this paper is to try and apply the insights gained in course of this my enterprise to a more general case of information processing in human brain and the challenging issue of human intelligence.