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Integrating Omics Data with Data Science Techniques to Accelerate Pharmaceutical Research and Development
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| Author:
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RAJESH MUNIRATHNAM
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| Abstract:
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The integration of multi-omics data with data science techniques represents a major advancement in pharmaceutical research. This study investigates the integration of genomic, proteomic, and metabolomic data with advanced data science techniques to enhance pharmaceutical research and development. By leveraging Multi-Omics Factor Analysis (MOFA), variational autoencoders (VAEs), and convolutional neural networks (CNNs), we significantly improved predictive accuracy, target identification, and biomarker discovery. Our
findings demonstrate that multi-omics integration provides a more comprehensive understanding of complex biological systems compared to traditional single-omics approaches. Case studies in breast cancer, Alzheimer’s
disease, and cardiovascular diseases highlight its potential to identify novel drug targets and biomarkers for early disease detection. However, challenges such as data heterogeneity and computational complexity remain,
necessitating further research and standardization. Overall, multi-omics integration offers transformative potential for personalized medicine and effective therapeutic strategies, underscoring the need for continued
innovation in this field.
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| Keyword:
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Multi-Omics Integration, Data Science Techniques, Genomics, Proteomics, Metabolomics, Drug Discovery, Biomarker Discovery
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.01.843
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| Download:
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Request For Article
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