Researchers from Genentech and Stanford University have developed an Iterative Perturb-seq Procedure leveraging machine learning for efficient design of perturbation experiments. The method facilitates the engineering of cells, sheds light on gene regulation, and predicts the results of perturbations. It also addresses the issue of active learning in a budget context for Perturb-seq data, demonstrating strong performance in gene and genome-scale screens. For more information, you can check out the Paper and Github. All credit for this research goes to the researchers of this project.
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Perturb-Seq: Leveraging AI for Efficient Design of Perturbation Experiments
Introduction
Researchers from Genentech and Stanford University have developed an innovative approach to Perturb-Seq, leveraging machine learning to efficiently design perturbation experiments.
Practical Solutions and Value
The Perturb-Seq method allows for the engineering of cells to a certain state, sheds light on gene regulation, and aids in identifying target genes for therapeutic intervention. Recent technological developments have augmented the efficiency, scalability, and breadth of Perturb-Seq.
Machine learning models can predict the results of perturbations, using pre-existing Perturb-Seq datasets to forecast the expression results of unseen perturbations, individual genes, or combinations of genes.
The researchers have introduced an approach that involves carrying out the Perturb-Seq assay in a wet-lab environment and implementing a machine learning model using an interleaving sequential optimal design approach. This approach allows for the creation of a model that has adequately explored the perturbation space with minimal perturbation experiments done.
To address the issue of active learning in a budget context for Perturb-Seq data, the team provides a novel approach termed ITERPERT (ITERative PERTurb-seq), which supplements data evidence with publicly available prior knowledge sources, particularly in the early stages and when funds are tight.
AI for Business Evolution
If you want to evolve your company with AI, stay competitive, and use AI for your advantage, consider leveraging the iterative Perturb-Seq procedure developed by Genentech and Stanford University researchers.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Researchers from Genentech and Stanford University Develop an Iterative Perturb-seq Procedure Leveraging Machine Learning for Efficient Design of Perturbation Experiments
- MarkTechPost
- Twitter – @itinaicom
