Machine learning may be the key to solving problems that genomic medicine is currently facing. One of the goals of genomic medicine is to determine how variations in DNA can affect the risk of specific diseases. Using machine learning to model the relationships between DNA and key molecules in the cell can help researchers study genomic medicine more efficiently.

Researchers may be able to leverage genomic data and machine learning to better predict disease risks through the use of large scale data sets and deep learning. Machine learning can be used to turn measurements into predictive models for ‘‘cell variables,’’ quantities that are relevant to cell function. By knowing how mutations affect disease via cell variables, diagnosticians and pharmacogeneticists can work to find the intricacies of the disease, develop treatments, and plan therapy for individual patients. Introducing these technologies to the study of the genome will greatly enhance the field of genomic medicine.

Written by IEEE on June 20, 2017

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Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine.