Small molecules, including drugs and chemical compounds, have a profound impact on gene expression within cells. Understanding how these molecules modulate gene expression in various cell types is crucial for drug discovery, personalized medicine, and gaining insights into cellular responses. This open problem focuses on developing predictive models and analytical approaches to decipher and anticipate how small molecules influence gene expression patterns in different cell types.
\nChallenges and Objectives:
\nHeterogeneity Across Cell Types: Investigate the heterogeneity of gene expression responses to small molecules across diverse cell types. Develop methods to categorize and cluster cell types based on their transcriptional profiles.
Predictive Modeling: Create machine learning and computational models that can predict gene expression changes induced by specific small molecules in different cell types. These models should consider factors like dosage, treatment duration, and molecular mechanisms of action.
Multi-Omics Integration: Integrate various omics data (e.g., single-cell RNA-seq, proteomics, epigenomics) to capture comprehensive cellular responses to small molecules. Explore how these molecular layers interact and influence gene expression.
Drug Repurposing: Identify potential drug candidates or repurposing opportunities by matching small molecules with specific gene expression profiles across cell types. This can lead to the discovery of novel therapeutic applications.
Interpretable Models: Develop interpretable models that provide insights into the underlying biological pathways and mechanisms through which small molecules affect gene expression. Explainable AI can enhance the utility of predictive models.
Scalability: Address scalability issues when dealing with large-scale single-cell datasets and compound libraries. Efficient algorithms and data preprocessing techniques are essential.
Small molecules, including drugs and chemical compounds, have a profound impact on gene expression within cells. Understanding how these molecules modulate gene expression in various cell types is crucial for drug discovery, personalized medicine, and gaining insights into cellular responses. This open problem focuses on developing predictive models and analytical approaches to decipher and anticipate how small molecules influence gene expression patterns in different cell types.
\nChallenges and Objectives:
\nHeterogeneity Across Cell Types: Investigate the heterogeneity of gene expression responses to small molecules across diverse cell types. Develop methods to categorize and cluster cell types based on their transcriptional profiles.
Predictive Modeling: Create machine learning and computational models that can predict gene expression changes induced by specific small molecules in different cell types. These models should consider factors like dosage, treatment duration, and molecular mechanisms of action.
Multi-Omics Integration: Integrate various omics data (e.g., single-cell RNA-seq, proteomics, epigenomics) to capture comprehensive cellular responses to small molecules. Explore how these molecular layers interact and influence gene expression.
Drug Repurposing: Identify potential drug candidates or repurposing opportunities by matching small molecules with specific gene expression profiles across cell types. This can lead to the discovery of novel therapeutic applications.
Interpretable Models: Develop interpretable models that provide insights into the underlying biological pathways and mechanisms through which small molecules affect gene expression. Explainable AI can enhance the utility of predictive models.
Scalability: Address scalability issues when dealing with large-scale single-cell datasets and compound libraries. Efficient algorithms and data preprocessing techniques are essential.