Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing

Pham, Thai-Hoang; Qiu, Yue; Liu, Jiahui; Zimmer, Steven; O’Neill, Eric; Xie, Lei; Zhang, Ping

doi:10.1016/j.patter.2022.100441

Cited by 12 publications

(12 citation statements)

References 52 publications

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“…In this section, we will compare the performance of TranSiGen with other baseline models in inferring differential expression genes. The models include DLEPS 10 , DeepCE 11 and CIGER 12 . Among them, DLEPS 10 predicts one DEG profiling for each compound without considering cell lines, while DeepCE 11 , CIGER 12 , and our model TranSiGen are all capable of inferring cellspecific DEG profiling.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we will compare the performance of TranSiGen with other baseline models in inferring differential expression genes. The models include DLEPS 10 , DeepCE 11 and CIGER 12 .…”

Section: Comparison With Existing Models In Inferring Differential Ex...mentioning

confidence: 99%

“…This model has been utilized to identify potential candidates for obesity, hyperuricemia and nonalcoholic steatohepatitis 10 . On the other hand, DeepCE 11 and CIGER 12 leveraged one-hot encoding to discriminate different cell types, and learned from perturbation profiles of various cells. These two models focused on predicting differential gene expression profiles disrupted by novel chemicals, and have been applied to the drug repurposing pipeline using drugs from the DrugBank database 11,12 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…In this section, we will compare the performance of TranSiGen with other baseline models in inferring differential expression genes. The models include DLEPS 10 , DeepCE 11 and CIGER 12 .…”

Section: Comparison With Existing Models In Inferring Differential Ex...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Tong,

Qu,

Kong

et al. 2023

Preprint

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“…Methodologically, e.g., Chemical-Induced Gene Expression Ranking (CIGER) 33 has been a recent development, which has been validated in the pancreatic cancer context, albeit only on cell lines, and comparison to other methods has not been performed in this study. Utilizing a successor of the original Connectivity Map, the Library of Integrated Network-based Cellular Signatures (LINCS), as a database, and with the aim to provide better benchmarking, recent work 34 identified homoharringtonine as a potential treatment for liver cancer, which was validated in vivo using xenograft and carbon tetrachloride-induced liver fibrosis models.…”

Section: Transcriptomics Datamentioning

confidence: 99%

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Pruteanu

Bender

2023

ACS Med. Chem. Lett.

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Gene expression and cell morphology data are highdimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.

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“…A great deal of effort has been devoted to collecting, annotating, and predicting chemical-induced transcriptomics profiles. 8 , 9 , 10 However, RNA expression alone may not capture a comprehensive picture of biological processes. Because molecular functions mainly manifest at a protein level, a proteomics profile may be better at characterizing and predicting cellular and organismal phenotypes than a transcriptomics profile.…”

Section: Introductionmentioning

confidence: 99%

Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing

Abstract: Highlights d A new deep-learning method (CIGER) for chemical-induced gene expression ranking d CIGER can predict gene expression for de novo chemicals from chemical structures d We discovered drugs for the treatment of drug-resistant pancreatic cancer

Cited by 12 publications

References 52 publications

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses

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