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Answer :
The application of Artificial Intelligence (AI) and Big Data in the field of biology comes with several challenges.
1. Data Acquisition and Quality: Gathering large-scale biological data is a significant challenge. Obtaining comprehensive and high-quality data from diverse sources such as genomics, proteomics, and clinical records requires extensive efforts. Ensuring, consistency, and standardization is crucial for reliable analysis and interpretation.
2. Data Integration and Interoperability: Biological data is often generated from multiple sources and different formats, making integration and interoperability challenging. Harmonizing diverse datasets to create a unified and standardized data infrastructure is necessary for effective AI and Big Data analytics in biology. Developing standardized ontologies and data exchange formats can facilitate data integration.
3. Computational Infrastructure and Storage: Biological data, especially genomics and imaging data, can be enormous in size and require substantial computational resources for processing and storage. High-performance computing infrastructure is essential to handle the computational demands of AI algorithms and store large datasets efficiently.
4. Algorithm Development and Interpretability: Developing robust AI algorithms for biological applications requires a deep understanding of biological processes and domain expertise. Algorithms must be tailored to handle the unique characteristics and complexities of biological data. Additionally, ensuring interpretability and explainability of AI models is crucial for gaining insights and building trust in their predictions.
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