AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Genetic Engineering

Showing 1 to 10 of 24 articles

Clear Filters

Sequence-to-function deep learning frameworks for engineered riboregulators.

Nature communications
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....

A deep learning approach to programmable RNA switches.

Nature communications
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...

A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.

Nature communications
The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed 'genetic engineering attribution', would deter misuse, yet i...

A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments.

Nature communications
A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human c...

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences.

ACS synthetic biology
N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current...

Synthetic biology and artificial intelligence in crop improvement.

Plant communications
Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved b...

Machine learning for synthetic gene circuit engineering.

Current opinion in biotechnology
Synthetic biology leverages engineering principles to program biology with new functions for applications in medicine, energy, food, and the environment. A central aspect of synthetic biology is the creation of synthetic gene circuits - engineered bi...

Artificial Intelligence-Based Approaches for AAV Vector Engineering.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Adeno-associated virus (AAV) has emerged as a leading vector for gene therapy due to its broad host range, low pathogenicity, and ability to facilitate long-term gene expression. However, AAV vectors face limitations, including immunogenicity and ins...

[Artificial intelligence-assisted design, mining, and modification of CRISPR-Cas systems].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review co...

Advances in AAV capsid engineering: Integrating rational design, directed evolution and machine learning.

Molecular therapy : the journal of the American Society of Gene Therapy
Adeno-associated virus (AAV) has emerged as a highly promising vector for human gene therapy due to its favorable safety profile, versatility, and ability to transduce a wide range of tissues. However, natural AAV serotypes have shortcomings, includi...