Variational Autoencoder for Anti-Cancer Drug Response Prediction

Dong, Hongyuan; Xie, Jiaqing; Jing, Zhi; Ren, Dexin

doi:10.48550/arxiv.2008.09763

Cited by 3 publications

(3 citation statements)

References 28 publications

(46 reference statements)

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“…The combined model was trained to predict drug response in cell lines. Another model uses separate AEs to encode cancer and drug features ( 127 ). A GeneVAE encodes gene expressions from CCLE dataset, and a Junction-Tree VAE (JT-VAE) ( 128 ) encodes drug molecular graphs from the ZINC database.…”

Section: Deep Learning Methods For Drug Response Predictionmentioning

confidence: 99%

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Partin

Brettin²,

Zhu³

et al. 2023

Front. Med.

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Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

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Section: Deep Learning Methods For Drug Response Predictionmentioning

confidence: 99%

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Partin

Brettin²,

Zhu³

et al. 2023

Front. Med.

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“…Chemical-induced transcriptional profiles directly associate molecular features with the cellular effect of a particular drug. This association is beneficial for characterizing drug response in different cells [25][26][27] . Here, we applied the TranSiGen-derived representation to predict the area under the dose-response curve (AUC) of a compound on a specific cell line.…”

Section: Drug Response Prediction With Transigen-derived Representationmentioning

confidence: 99%

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery

Tong,

Qu,

Kong

et al. 2024

Nat Commun

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Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.

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“…Chemical-induced transcriptional profiles directly associate molecular features with the cellular effect of a particular drug. This association is beneficial for characterizing drug response in different cells [21][22][23] . Here, we applied the TranSiGen-derived representation to predict the area under the dose-response curve (AUC) of a compound on a specific cell line.…”

Section: Drug Response Prediction With Transigen-derived Representationmentioning

confidence: 99%

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Tong,

Qu,

Kong

et al. 2023

Preprint

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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.

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Variational Autoencoder for Anti-Cancer Drug Response Prediction

Cited by 3 publications

References 28 publications

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

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