Machine Learning Offers Promise for Precision Medicine, But Interpretability and Data Complexity Remain Key Challenges
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Recent advances in genomics have improved disease understanding, but challenges persist in capturing complex biological interactions and predicting outcomes.
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Omics datasets are valuable for precision medicine but introduce analytical complexities, affected by both general and specific issues.
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Machine learning and deep learning are critical for analyzing complex omics data, but face interpretability challenges.
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Deep learning models like CNNs offer capabilities to identify interactions, model non-linear effects, and integrate heterogeneous data.
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Approaches like DeepInsight and DeepFeature pioneer omics data analysis, but challenges around interpretability, data heterogeneity, and model optimization remain.