We all know that integrating computational analysis extraction leads to practical applications such as Machine Learning for Bioinformatics during the drug discovery process. Machine Learning could make in silico analysis more relevant since it can be a complementary technique that will lead to pre-clinical trials, enhancing competitiveness in drug development. Machine Learning for Bioinformatics can cut costs by predicting the efficacy of the new drug first before rigour of clinical trials and other tests.
Over many years, UM researchers have been collecting extensive and diverse data from various sources to enhance their set of Machine Learning for Bioinformatics automation protocol as part of research and client services. What has helped them is to automate repetitive data processing and analysis tasks apply at several steps during early drug discovery to:
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