In the rapidly evolving field of biomedical research, machine learning has emerged as a transformative tool that enhances our ability to analyze complex biological data. By leveraging algorithms that can learn from and make predictions based on data, researchers can uncover insights that were previously unattainable through traditional statistical methods. This capability is particularly crucial in understanding the complexities of diseases, guiding diagnosis, and developing targeted therapies.
I summarize information on the application of machine learning methods in biomedical research in this post, drawing from the experiences I gained while participating in the Louisiana Biomedical Research Network (LBRN) Summer Bioinformatics Workshop in 2018.
Discover some supervised machine learning techniques and how they could be applied in biomedical research to classify disease/cell types and predict outcomes here.
Explore unsupervised machine learning techniques used to discover hidden patterns in biological data here.