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A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

ABSTRACT: A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

PROVIDER: PRJNA693494 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR13489972_1.fastq.gz	Fastqsanger.gz
	SRR13489972_2.fastq.gz	Fastqsanger.gz
	SRR13489973_1.fastq.gz	Fastqsanger.gz
	SRR13489973_2.fastq.gz	Fastqsanger.gz
	SRR13489974_1.fastq.gz	Fastqsanger.gz

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

Project description:Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.

2023-03-31 | GSE165171 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery

Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

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A Deep Learning based Efficacy Prediction System for Drug Discovery

Project description:This SuperSeries is composed of the SubSeries listed below.

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Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning

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Deep Learning Models for Cell Cycle Phase Prediction from Single-Cell RNA Sequencing Data

Project description:Accurate prediction of cell cycle phases is important in understanding how certain diseases (e.g. cancer) develop and respond to treatment. It is also essential for reducing the confounding effects in single-cell RNA sequencing (scRNA-Seq) data analysis. We evaluated both traditional machine learning techniques and deep learning approaches for predicting cell cycle phases from scRNA-seq data. Models were trained on consensus predictions from four popular cell cycle analysis tools, namely, CellCycleScore, ccAF, Revelio, and Tricycle, then applied to unlabeled scRNA-seq data generated from human leukemia cell lines. We evaluated model performance using publicly available datasets, GSE146773 (human leukemia cells, 1,151 cells) and GSE64016 (human embryonic stem cells, 213 cells), both labeled with their respective cell cycle phases through experiments. In addition to the four tools, we assessed traditional machine learning models (AdaBoost, Random Forest, and LightGBM), deep learning models including multiple dense networks DNN (2-5), CNN, Hybrid CNN-Dense, Feature embedding, and ensemble models. DNN 3 achieved the highest accuracy, reaching 75.16% on the GSE146773 dataset and 70.85% on GSE64016. This outperformed existing tools such as Revelio (68.34% on GSE146773, 68.2% on GSE64016), CellCycleScore (66.54% on GSE146773, 53.84% on GSE64016), ccAF (45% on GSE146773, 40% on GSE64016), ccAFv2 (47.23% on GSE146773, 43.72% on GSE64016), Tricycle (53.23% on GSE146773, 58.7% on GSE64016), and Cyclum (38.52% on GSE146773, 26.3% for GSE64016). These results signify the potential of deep learning models for robust cell cycle phase prediction in scRNA-seq data.

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