Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cell type transcriptomic modules reveal shared molecular mechanisms in Alzheimer's and Parkinson's disease.

GigaScience·2026

Same author

Intra- and Inter-Rater Reliability and Validation of Orofacial Cleft Classification in the Cleft Collective Cohort Study.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2026

Same author

Frequency-reconfigurable aperture-coupled magneto-electric dipole antenna for multi-band sub-6 GHz 5G NR systems.

Scientific reports·2026

Same author

Functionalised mesoporous silica with stearic acid: A novel approach to improve octreotide delivery.

International journal of pharmaceutics·2026

Same author

Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.

Communications biology·2026

Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same journal

Spatially resolved profiling of extracellular vesicles in tissues with Spatial-EV-seq.

Nature biotechnology·2026

Same journal

Mapping the spatial landscape of extracellular vesicles in tissues with Spatial-EV-seq.

Nature biotechnology·2026

Same journal

Author Correction: Generation of modified cows and sheep from spermatid-like haploid embryonic stem cells.

Nature biotechnology·2026

Same journal

Toward mechanistic virtual immune cells.

Nature biotechnology·2026

Same journal

Mapping and engineering the human cell-cell interactome.

Nature biotechnology·2026

Same journal

Efficient generation of epitope-targeted antibodies with Germinal.

Nature biotechnology·2026

See all related articles

Search research articles

Home
Txpert: Using Multiple Knowledge Graphs For Prediction Of Transcriptomic Perturbation Effects.

Home
Txpert: Using Multiple Knowledge Graphs For Prediction Of Transcriptomic Perturbation Effects.

Related Experiment Video

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects.

Frederik Wenkel^1,2, Wilson Tu^3,4, Cassandra Masschelein^3,4

¹Valence Labs, Montréal, Quebec, Canada. frederik@valencelabs.com.

Nature Biotechnology

|May 1, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Predicting cellular responses to genetic perturbations is crucial for disease research. TxPert, a new deep learning method, accurately forecasts these effects using gene relationship knowledge graphs, improving predictions for unseen genetic changes.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Related Experiment Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computational biology
Genomics
Systems biology

Background:

Accurate prediction of cellular responses to genetic perturbations is vital for understanding diseases and developing therapies.
Exploring all possible genetic perturbations is costly, necessitating methods that generalize to new conditions.

Purpose of the Study:

To develop a deep learning method, TxPert, for predicting transcriptomic perturbation effects.
To leverage multiple knowledge graphs of gene relationships for enhanced prediction accuracy.

Main Methods:

TxPert utilizes a latent-transfer-based deep learning approach.
It integrates information from multiple knowledge graphs, including biological databases and high-throughput screens.
The method focuses on predicting gene (product)-gene (product) relationships.

Main Results:

Combining different knowledge graphs improved prediction performance.
TxPert achieved performance comparable to experimental reproducibility for single unseen perturbations.
For double unseen perturbations and cross-cell line predictions, TxPert outperformed existing methods by 8-25%.

Conclusions:

TxPert offers a powerful deep learning framework for predicting transcriptomic responses to genetic perturbations.
The integration of diverse knowledge graphs enhances the generalizability and accuracy of perturbation effect predictions.
This method holds promise for accelerating therapeutic development and biological discovery.