training data updated into AI models

The development of artificial intelligence (AI) technologies is crucial for the discovery of new medications, and these technologies have applications in all phases of the drug discovery process, including target selection, biomarker screening, and data analysis in pre-clinical and clinical trials. According to earlier research, AI-based methods require a lot of training data in order to accurately anticipate the test data. To support data-driven decision making in the drug discovery process, machine learning (ML) models have been routinely employed to collect digital pathology data in this situation. Meanwhile, a lot of work has gone into creating cutting-edge machine learning (ML) techniques, such as deep learning (DL) and neural network tools, in order to provide the pharmaceutical business with a sufficient amount of high-dimensional data.

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