Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Deep Learning based Efficacy Prediction System for Drug Discovery

ABSTRACT: A Deep Learning based Efficacy Prediction System for Drug Discovery

PROVIDER: PRJNA693487 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			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

Items per page:

1 - 5 of 54

Similar Datasets

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

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

| PRJNA693497 | ENA

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

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

| PRJNA693496 | ENA

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

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

| PRJNA693494 | ENA

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

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 | GSE165174 | GEO

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

2023-03-31 | GSE165173 | GEO

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

2023-03-31 | GSE165171 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery

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

2023-03-31 | GSE165175 | GEO

Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression

Project description:Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression

| PRJNA732423 | ENA

Machine learning for discovery: deciphering RNA splicing logic

Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.

2022-10-01 | GSE200096 | GEO

MULocDeep: An Interpretable Deep Learning Model for Protein Localization Prediction with Sub-organelle Resolution

Project description:Prediction of protein localization plays an important role in understanding protein function and mechanism. A deep learning-based localization prediction tool (“MULocDeep”) assessing each amino acid’s contribution to the localization process provides insights into the mechanism of protein sorting and localization motifs. A dataset with 45 sub-organellar localization annotations under 10 major sub-cellular compartments was produced and the tool was tested on an independent dataset of mitochondrial proteins that were extracted from Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots, and analyzed by shotgun mass spectrometry.

2022-02-15 | PXD019987 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data