Machine Learning in Molecular Biology
summary
Addresses why standard off-the-shelf machine learning tools often fail on molecular biology problems—difficulty in formulating ML tasks and rapidly changing high-throughput data types—and presents Bayesian probabilistic models as a solution. Covers designing probabilistic models and learning algorithms for genomics and discusses the role of feature selection in both supervised and unsupervised learning.
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