netDx: interpretable patient classification using integrated patient similarity networks
Abstract Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clini...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2019-03-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20188497 |
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| _version_ | 1849389036927451136 |
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| author | Shraddha Pai Shirley Hui Ruth Isserlin Muhammad A Shah Hussam Kaka Gary D Bader |
| author_facet | Shraddha Pai Shirley Hui Ruth Isserlin Muhammad A Shah Hussam Kaka Gary D Bader |
| author_sort | Shraddha Pai |
| collection | DOAJ |
| description | Abstract Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows. |
| format | Article |
| id | doaj-art-0309d2c4a4e24c699be79e46aa0bf371 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-0309d2c4a4e24c699be79e46aa0bf3712025-08-20T03:42:04ZengSpringer NatureMolecular Systems Biology1744-42922019-03-0115311110.15252/msb.20188497netDx: interpretable patient classification using integrated patient similarity networksShraddha Pai0Shirley Hui1Ruth Isserlin2Muhammad A Shah3Hussam Kaka4Gary D Bader5The Donnelly Centre, University of TorontoThe Donnelly Centre, University of TorontoThe Donnelly Centre, University of TorontoThe Donnelly Centre, University of TorontoThe Donnelly Centre, University of TorontoThe Donnelly Centre, University of TorontoAbstract Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.https://doi.org/10.15252/msb.20188497multimodal data integrationmulti‐omicspatient similarity networksprecision medicinesupervised machine learning |
| spellingShingle | Shraddha Pai Shirley Hui Ruth Isserlin Muhammad A Shah Hussam Kaka Gary D Bader netDx: interpretable patient classification using integrated patient similarity networks Molecular Systems Biology multimodal data integration multi‐omics patient similarity networks precision medicine supervised machine learning |
| title | netDx: interpretable patient classification using integrated patient similarity networks |
| title_full | netDx: interpretable patient classification using integrated patient similarity networks |
| title_fullStr | netDx: interpretable patient classification using integrated patient similarity networks |
| title_full_unstemmed | netDx: interpretable patient classification using integrated patient similarity networks |
| title_short | netDx: interpretable patient classification using integrated patient similarity networks |
| title_sort | netdx interpretable patient classification using integrated patient similarity networks |
| topic | multimodal data integration multi‐omics patient similarity networks precision medicine supervised machine learning |
| url | https://doi.org/10.15252/msb.20188497 |
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