perturbation response prediction

Alpha-Frag improves peptide identification by predicting fragment ion presence with a deep neural network, enhancing detection rates in DDA and DIA data.

This paper introduces a multi-agent directed evolution approach that identifies hidden mutations by using multiple substrates, improving enzyme engineering efficiency.

The paper introduces BRED, a bioluminescence resonance energy transfer system to monitor ceramide trafficking, demonstrating its application in tracking ceramide movement to the Golgi using flow cytometry.

This paper engineers a single Escherichia coli strain to stabilize microbial consortia through competitive exclusion and toxin secretion, showing tunable population composition.

This study investigates whether deep neural networks perceive visual stimuli similarly to humans by testing if they exhibit known perceptual phenomena without explicit training.

The paper demonstrates a CRISPRa/i toolbox for manipulating the Cacna1h gene promoter to study CaV3.2 channelopathies in neuronal cells and tissues.

This study identifies distinct GPe neuron subtypes using mouse lines and shows their unique roles in motor behavior and anxiety-like behavior through optogenetics and machine learning.

This paper introduces a dCas9 regulator that reduces competition among sgRNAs in CRISPRi circuits, enabling independent gene repression control.

This paper describes the development of a modified Atezolizumab (Maxatezo) with improved anti-tumor efficacy and reduced anti-drug antibody formation in vivo.

The paper uses deep mutational scanning to analyze how mutations affect -1PRF in alphavirus polyproteins, revealing coordination between transcript and nascent chain features.

This study shows that cobicistat synergizes with remdesivir to inhibit SARS-CoV-2 replication in cell lines and organoids, reducing viral replication to levels comparable to uninfected cells.

This study identifies calcium signaling as a key regulator of IgE+ plasma cell differentiation and survival through whole-genome CRISPR screening.

This study uses dynamic perturbation to manipulate neural dynamics during working memory retention, showing it alters memory strength through synaptic plasticity.

The paper develops a method to infer patient-specific gene perturbation profiles from cancer data using causal networks and shows its effectiveness in simulations and real cancer samples.

This study uses CRISPR-Cas13a to target and downregulate BCR-ABL in CML cells, reducing downstream gene expression and cell viability.

This paper develops algorithms to predict drug response and interactions in tuberculosis by analyzing transcriptome signatures of cell viability.

This study investigates how high-definition tDCS applied to the cerebellum or prefrontal cortex affects cognitive and motor tasks, finding load-specific effects and cerebellar involvement in verbal working memory and sequence learning.

The paper uses genome-scale CRISPR screening to identify genetic modifiers of LDL uptake in hepatocytes, finding several new genes involved in this process.

This paper introduces DeepCoy, a deep learning method to generate decoy molecules that match active molecules' properties, improving virtual screening performance by reducing bias and enhancing benchmarking.

The paper develops a CRISPR/Cas9-based tool to induce targeted and tunable DNA double-strand breaks in human cells for studying repair mechanisms.

MAESTRO enables efficient and accurate tracking of thousands of low-frequency mutations with reduced sequencing, improving mutation testing in cancer and liquid biopsies.

This paper develops nanobodies to control gene expression and epigenetic memory by recruiting chromatin regulators to specific genomic locations.

This study compares shRNA and CRISPR/Cas13d screens for circRNA function, finding that optimized CRISPR/Cas13d reduces false positives and identifies true functional circRNAs.

The paper introduces CRISPR-based tools to manipulate phosphoinositide signalling proteins in Drosophila and identifies new molecules involved in eye development.

The paper predicts gene responses to TF perturbations using TF-independent features, finding that gene expression level and variation are more predictive than TF binding or histone marks.

DeepCellState predicts cell type-specific transcriptional states after drug treatment using an autoencoder, showing improved accuracy over baselines and generalizing to new cell types and interventions.

CiBER-seq quantifies genetic perturbation effects on gene expression in yeast by linking CRISPRi guides to phenotypes via barcoded sequencing.

This study uses CRISPR-Cas13 to functionally screen circRNAs, identifying roles in cell growth and development.

The paper identifies low micromolar SARS-CoV-2 Mpro inhibitors from in silico screens through biochemical testing of synthesized compounds.

The paper introduces FLiCRE, a molecular tool for tagging calcium-activated cells, and uses it to identify and manipulate a striatal cell-type involved in aversion behavior.