AIMC Topic: Biotechnology

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Logic-enabled textiles.

Proceedings of the National Academy of Sciences of the United States of America
Textiles hold great promise as a soft yet durable material for building comfortable robotic wearables and assistive devices at low cost. Nevertheless, the development of smart wearables composed entirely of textiles has been hindered by the lack of a...

The future of Artificial Intelligence for the BioTech Big Data landscape.

Current opinion in biotechnology
Recent Industry 4.0 advancements are making available massive amounts of data for the development of innovative BioTech solutions. However, several challenges need to be overcome to correctly use data and novel, non-pharma technologies to greatly spe...

Machine learning to navigate fitness landscapes for protein engineering.

Current opinion in biotechnology
Machine learning (ML) is revolutionizing our ability to understand and predict the complex relationships between protein sequence, structure, and function. Predictive sequence-function models are enabling protein engineers to efficiently search the s...

Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity.

Nature communications
Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning t...

Computing within bacteria: Programming of bacterial behavior by means of a plasmid encoding a perceptron neural network.

Bio Systems
In nature, bacteria exhibit a limited repertoire of behaviors in response to environmental changes. Synthetic biology has now opened up the possibility of programming cells or unicellular organisms in order to enable them to perform certain tasks, wh...

Forty years of directed evolution and its continuously evolving technology toolbox: A review of the patent landscape.

Biotechnology and bioengineering
Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for decades. Rational design and directed evolution are two major pathways to achieve the desired ends. Whi...

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina.

Biotechnology advances
Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements...

Vital signal sensing and manipulation of a microscale organ with a multifunctional soft gripper.

Science robotics
Soft grippers that incorporate functional materials are important in the development of mechanically compliant and multifunctional interfaces for both sensing and stimulating soft objects and organisms. In particular, the capability for firm and deli...

Artificial intelligence for microbial biotechnology: beyond the hype.

Microbial biotechnology
It has been a landmark year for artificial intelligence (AI) and biotechnology. Perhaps the most noteworthy of these advances was Google DeepMind's AlphaFold2 algorithm which smashed records in protein structure prediction (Jumper et al., 2021, Natur...

Artificial intelligence and the future of life sciences.

Drug discovery today
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials;...