Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD

ABSTRACT: Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD

PROVIDER: PRJNA1162729 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD

Project description:Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation with smoke exposure being a major risk factor. To define previously unknown biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single cell-RNA sequencing data from the gold standard mouse smoke exposure model to identify significant latent factors (context-specific co-expression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and new biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from COPD patients, and smoke exposure resulted in enhanced GSN release from airway cells from COPD patients. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provide a translational analytical platform for other diseases.

2024-10-14 | GSE277533 | GEO

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (RNA-Seq)

Project description:High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntington’s disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds’ structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts.

2020-01-22 | GSE129143 | GEO

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (ChIP-Seq)

2020-01-22 | GSE129141 | GEO

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

Project description:Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

| PRJNA956958 | ENA

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning I

Project description:Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning I

| PRJNA956962 | ENA

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning III

Project description:Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning III

| PRJNA956965 | ENA

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning II

Project description:Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning II

| PRJNA956963 | ENA

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning IV

Project description:Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning IV

| PRJNA956964 | 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

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

Project description:A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

| PRJNA530233 | ENA