TranSiGen: Deep representation learning of chemical-induced transcriptional profile
Tong, X.; Qu, N.; Kong, X.; Ni, S.; Wang, K.; Zhang, L.; Wen, Y.; Zhang, S.; Li, X.; Zheng, M.
Show abstract
With the advancement of high-throughput RNA sequencing technologies, the use of chemical-induced transcriptional profiling has greatly increased in biomedical research. However, the usefulness of transcriptomics data is limited by inherent random noise and technical artefacts that may cause systematical biases. These limitations make it challenging to identify the true signal of perturbation and extract knowledge from the data. In this study, we propose a deep generative model called Transcriptional Signatures Generator (TranSiGen), which aims to denoise and reconstruct transcriptional profiles through self-supervised representation learning.TranSiGen uses cell basal gene expression and compound molecular structure representation to infer the chemical-induced transcriptional profile. Results demonstrate the effectiveness of TranSiGen in learning and predicting differential expression genes. The representation derived from TranSiGen can also serve as an alternative phenotype information, with applications in ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. We envisage that integrating TranSiGen into the drug discovery and mechanism research pipeline will promote the development of biomedicine.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A Transferable Deep Learning Approach to Fast Screen Potent Antiviral Drugs against SARS-CoV-2 97%
- Predicting anti-cancer drug synergy using extended drug similarity profiles 95%
- Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy 95%
Similar papers in this journal
- AutoMolDesigner for Antibiotic Discovery: An AI-based Open-source Software for Automated Design of Small-molecule Antibiotics 96%
- DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model 96%
- MolAI: A Deep Learning Framework for Data-driven Molecular Descriptor Generation and Advanced Drug Discovery Applications 96%
Similar papers in this journal
- Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data 97%
- Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds 95%
- BitterMatch: Recommendation systems for matching molecules with bitter taste receptors 94%
Similar papers in this journal
Similar papers in this journal
- Thinking like a structural biologist: A pocket-based 3D molecule generative model fueled by electron density 96%
- Interpretable Deep Learning for Improving Cancer Patient Survival Based on Personal Transcriptomes 95%
- Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.