kover | Learn interpretable computational phenotyping models | Machine Learning library

kover is a Python library typically used in Healthcare, Pharma, Life Sciences, Artificial Intelligence, Machine Learning applications. kover has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However kover build file is not available. You can download it from GitHub.

Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validated through an application to the genomic prediction of antimicrobial resistance, an important public health concern. Highly accurate models were obtained for 12 species and 56 antibiotics, and their interpretation revealed known resistance mechanisms, as well as some potential new ones. An open-source disk-based implementation that is both memory and computationally efficient is included with this work. The implementation is turnkey, requires no prior knowledge of machine learning, and is complemented by comprehensive tutorials. Drouin, A., Letarte, G., Raymond, F., Marchand, M., Corbeil, J., & Laviolette, F. (2019). Interpretable genotype-to-phenotype classifiers with performance guarantees. Scientific Reports, 9(1), 4071. [PDF]. Drouin, A., Giguère, S., Déraspe, M., Marchand, M., Tyers, M., Loo, V. G., Bourgault, A. M., Laviolette, F. & Corbeil, J. (2016). Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. BMC Genomics, 17(1), 754. [PDF].