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Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction
Xiaodi Li1, Trisha Das1,2, Kritib Bhattarai1,3
1Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
Abstract:
Researchers have developed pharmacogenomics datasets for various purposes, such as biomarker identification, yet drug response prediction models often underperform due to dataset inconsistencies. These variations arise from inter-tumoral heterogeneity, experimental conditions, and cell subtype complexity, limiting model generalizability. To address this, we propose a computational model based on Aggregated Learning (AL) to enhance drug response prediction by learning from inconsistencies across multiple datasets. Our model minimizes discrepancies by training on overlapping inconsistent data points from three pharmacogenomic datasets-CCLE, GDSC2, and gCSI. Compared to four baseline methods-Selecting Better (SB), Result Average (RA), Combining Data (CD), and Model Average (MA)-our approach achieved superior performance with lower Mean Absolute Error (MAE) scores: 0.090 (CCLE-GDSC), 0.096 (CCLE-gCSI), and 0.081 (GDSC-gCSI). These results demonstrate that addressing inconsistencies enhances prediction accuracy and generalizability, making our model a promising solution for robust drug response predictions.
Insights
This study introduces Aggregated Learning (AL), a computational model that improves drug response prediction by learning from inconsistencies across multiple pharmacogenomics datasets. The novel approach enhances model accuracy and generalizability for biomarker identification.
Area of Science:
- Computational biology
- Pharmacogenomics
- Biomedical data science
Background:
- Pharmacogenomics datasets are crucial for biomarker identification and drug response prediction.
- Existing models often underperform due to inconsistencies stemming from inter-tumoral heterogeneity, experimental variations, and cell subtype complexity.
- These inconsistencies limit the generalizability of predictive models.
Purpose of the Study:
- To develop a computational model that enhances drug response prediction by effectively learning from inconsistencies across multiple pharmacogenomics datasets.
- To improve the accuracy and generalizability of predictive models by addressing data variations.
Main Methods:
- Proposed a novel computational model based on Aggregated Learning (AL).
- The AL model was trained on overlapping inconsistent data points from three pharmacogenomic datasets: Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC2), and cancer-genomics.org (gCSI).
- Compared the AL model's performance against four baseline methods: Selecting Better (SB), Result Average (RA), Combining Data (CD), and Model Average (MA).
Main Results:
- The Aggregated Learning (AL) model demonstrated superior performance compared to baseline methods.
- Achieved lower Mean Absolute Error (MAE) scores: 0.090 for CCLE-GDSC, 0.096 for CCLE-gCSI, and 0.081 for GDSC-gCSI.
- The results indicate that explicitly addressing dataset inconsistencies significantly enhances prediction accuracy.
Conclusions:
- Addressing inconsistencies within and across pharmacogenomics datasets is critical for improving drug response prediction models.
- The proposed Aggregated Learning (AL) model offers a promising solution for robust and generalizable drug response predictions.
- This approach has the potential to advance personalized medicine through more accurate biomarker identification and treatment selection.
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