Version 1
: Received: 14 February 2020 / Approved: 17 February 2020 / Online: 17 February 2020 (04:12:20 CET)
How to cite:
Azodi, C.B.; Tang, J.; Shiu, S. Opening the Black Box: Interpretable Machine Learning for Geneticists. Preprints2020, 2020020239. https://doi.org/10.20944/preprints202002.0239.v1.
Azodi, C.B.; Tang, J.; Shiu, S. Opening the Black Box: Interpretable Machine Learning for Geneticists. Preprints 2020, 2020020239. https://doi.org/10.20944/preprints202002.0239.v1.
Cite as:
Azodi, C.B.; Tang, J.; Shiu, S. Opening the Black Box: Interpretable Machine Learning for Geneticists. Preprints2020, 2020020239. https://doi.org/10.20944/preprints202002.0239.v1.
Azodi, C.B.; Tang, J.; Shiu, S. Opening the Black Box: Interpretable Machine Learning for Geneticists. Preprints 2020, 2020020239. https://doi.org/10.20944/preprints202002.0239.v1.
Abstract
Machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available because of its ability to find complex patterns in high dimensional and heterogeneous data. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, recent efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights using ML. Here we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.
Keywords
interpretable machine learning; deep learning; predictive biology
Subject
BIOLOGY, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
