Polly

Upload and harmonize multi-omics data (e.g., RNA-seq, proteomics) into Polly.

Use Polly's analysis tools to identify differentially expressed genes and proteins.

Integrate expression data to extract molecular signatures associated with the disease of interest.

Validate potential targets by exploring their interactions and pathways using Polly's knowledge graph.

Load patient data, including multi-omics profiles and clinical outcomes, into Polly.

Train machine learning classifiers on harmonized data to identify potential biomarkers.

Evaluate the predictive performance of the biomarkers using cross-validation techniques.

Validate the biomarkers in independent datasets to confirm their clinical utility.

Gather patient data, including genomic, transcriptomic, and proteomic profiles.

Use Polly's machine learning tools to train classifiers on harmonized data.

Segment patient cohorts based on their multi-omics profiles and clinical characteristics.

Identify subgroups of patients who are most likely to respond to specific treatments.

Import expression data from multiple sources into Polly.

Harmonize the data to remove batch effects and ensure comparability.

Perform meta-analysis to identify consistent patterns of gene expression across studies.

Extract molecular signatures that are robust across multiple datasets.

Input a gene signature of interest into Polly.

Search Polly's database for related indications and diseases.

Identify drugs that have the potential to reverse the disease signature.

Prioritize drugs for further investigation based on their potential efficacy and safety.