perturbation response prediction

This study shows that reducing mutant Lrrk2 in macrophages decreases lysosomal tubule formation and pro-inflammatory responses, suggesting potential therapeutic benefits for LRRK2-related Parkinson's but possible immune system side effects.

This study compares robotic and manual injection methods for CRISPR-CasRx RNA knockdown in zebrafish embryos, finding similar efficiency and phenotype rates between approaches.

CloMu is a reinforcement learning-based model for predicting cancer clonal evolution, outperforming existing methods in mutation causality and fitness prediction.

The paper introduces a CRISPR/Cas9 HDR plasmid platform for Drosophila with higher efficiency than existing methods, enabling new genetic experiments.

This study shows that combining FAK and PI3K inhibitors synergistically suppresses cervical and pancreatic cancer cell growth by increasing apoptosis and reducing mitosis.

The paper develops a modular exosome-based platform for targeted CRISPR/Cas9 genome editing, demonstrating efficient delivery and gene editing in vitro and in vivo.

CasTuner enables analog tuning of endogenous gene expression using CRISPR/Cas systems and a degron domain, showing dose-dependent repression and application in studying gene dosage effects.

The paper constructs a CRISPR library of E. coli mutants to identify temperature-sensitive mutants and demonstrates their use in engineering bacterial metabolite production.

This paper introduces OntoVAE, a VAE that incorporates ontologies to predict genetic and drug-induced perturbations using bulk and single-cell transcriptomic data.

This paper develops a SARS-CoV-2 reverse genetics system and lung cell lines for studying virus replication, antiviral screening, and host interactions.

This study shows that a variant of IL-2 (IL-2v) induces a stem-like, metabolically active T-cell state with improved therapeutic potential in adoptive cell therapy.

The paper introduces ePRINT, a method to globally map RBP-RNA interactions without purifying individual RBPs, revealing direct and indirect targets and differential activation during cell fate transitions.

CRISPRδ uses dCas13 to specifically block translation without affecting mRNA stability, achieving high gene knockdown specificity in mammalian cells.

TransCRISPR designs sgRNAs targeting DNA motifs in user-provided genomic regions across 30 genomes, validated for disrupting MYC binding sites and affecting gene expression.

The paper identifies causal regulatory elements in complex genetic loci using CRISPR perturbations and shows that many causal variants lack classical epigenetic markers.

This study repurposes the type I-G CRISPR system for bacterial genome engineering, showing that Cas3 helicase activity is not essential for immunity and that the system can improve homology-directed repair efficiency.

ICAT improves identification of cell states after perturbations in single-cell RNA sequencing by using self-supervised feature weighting and control-guided clustering.

The paper uses computational modeling to identify key brain regions for personalized neurostimulation strategies to improve cognitive interventions in Alzheimer's disease.

The paper develops a CRISPR/Cas9-mediated knock-in method in African turquoise killifish for precise genomic modifications and cell-type-specific expression.

This paper introduces scVAEDer, a deep-learning model combining variational autoencoders and diffusion models to generate meaningful embeddings for single-cell transcriptomics data, enabling data generation, perturbation prediction, and regulator detection.

The paper develops a pooled CRISPR activation screening platform in Drosophila cells to identify rapamycin resistance genes, finding three novel genes involved in RTK-Akt-mTOR pathway activation.

This study shows that combining KRASG12C inhibitors with PAK4 inhibitor KPT9274 enhances antitumor activity and survival in preclinical models of KRASG12C mutant pancreatic and lung cancers.

The paper proposes new treatment effect estimation methods using L0 norm-based lasso and ridge regression to identify subgroups of patients who may respond to a new treatment.

This study examines how alertness affects perceptual detection and discrimination across four experiments, finding reduced sensitivity and precision in discrimination tasks with lower alertness.

PerturbX predicts transcriptional responses to perturbations in new cellular contexts using deep learning trained on high-throughput screens.

TRAIL quantifies protein and cell lifetimes across tissues, showing cell division drives non-selective turnover in proliferative tissues while physicochemical features influence turnover in non-proliferative tissues.

The paper assesses the impact of CRISPR perturbations on vascular endothelial cell functions at CAD loci, identifying variants that significantly affect endothelial phenotypes.

The paper introduces the SIGER system to control protein expression by decoupling translation initiation features from target genes using inteins, improving protein yield and quality.

This paper introduces a molecular glue approach using pomalidomide to control the half-life of CRISPR-based technologies, enabling precise regulation of their activity and therapeutic potential.

This paper introduces a high-throughput platform for identifying host binding factors and antiviral drug targets by overcoming limitations in host plasma membrane complexity.