AI Medical Compendium Topic

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

CRISPR-Cas Systems

Showing 61 to 70 of 86 articles

Clear Filters

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

ACS nano
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for l...

Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.

Briefings in bioinformatics
The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures hav...

Evaluation of off-targets predicted by sgRNA design tools.

Genomics
The ease of programming CRISPR/Cas9 system for targeting a specific location within the genome has paved way for many clinical and industrial applications. However, its widespread use is still limited owing to its off-target effects. Though this off-...

Assessing Public Opinion on CRISPR-Cas9: Combining Crowdsourcing and Deep Learning.

Journal of medical Internet research
BACKGROUND: The discovery of the CRISPR-Cas9-based gene editing method has opened unprecedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the...

Machine-learning approach expands the repertoire of anti-CRISPR protein families.

Nature communications
The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechani...

CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

BMC bioinformatics
BACKGROUND: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using ...

Deep learning improves the ability of sgRNA off-target propensity prediction.

BMC bioinformatics
BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9...

Predicting CRISPR/Cas9-Induced Mutations for Precise Genome Editing.

Trends in biotechnology
SpCas9 creates blunt end cuts in the genome and generates random and unpredictable mutations through error-prone repair systems. However, a growing body of recent evidence points instead to Cas9-induced staggered end generation, nonrandomness of muta...

Revisiting CRISPR/Cas-mediated crop improvement: Special focus on nutrition.

Journal of biosciences
Genome editing (GE) technology has emerged as a multifaceted strategy that instantaneously popularised the mechanism to modify the genetic constitution of an organism. The clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-a...