Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929504650264576 |
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| author | Overbeek, Jamie C. Partin, Alexander Brettin, Thomas S. Chia, Nicholas Narykov, Oleksandr Vasanthakumari, Priyanka Wilke, Andreas Zhu, Yitan Clyde, Austin Jones, Sara Gnanaolivu, Rohan Liu, Yuanhang Jiang, Jun Wang, Chen Knutson, Carter McNaughton, Andrew Kumar, Neeraj Fernando, Gayara Demini Ghosh, Souparno Sanchez-Villalobos, Cesar Zhang, Ruibo Pal, Ranadip Weil, M. Ryan Stevens, Rick L. |
| author_facet | Overbeek, Jamie C. Partin, Alexander Brettin, Thomas S. Chia, Nicholas Narykov, Oleksandr Vasanthakumari, Priyanka Wilke, Andreas Zhu, Yitan Clyde, Austin Jones, Sara Gnanaolivu, Rohan Liu, Yuanhang Jiang, Jun Wang, Chen Knutson, Carter McNaughton, Andrew Kumar, Neeraj Fernando, Gayara Demini Ghosh, Souparno Sanchez-Villalobos, Cesar Zhang, Ruibo Pal, Ranadip Weil, M. Ryan Stevens, Rick L. |
| contents | Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data Overbeek, Jamie C. Partin, Alexander Brettin, Thomas S. Chia, Nicholas Narykov, Oleksandr Vasanthakumari, Priyanka Wilke, Andreas Zhu, Yitan Clyde, Austin Jones, Sara Gnanaolivu, Rohan Liu, Yuanhang Jiang, Jun Wang, Chen Knutson, Carter McNaughton, Andrew Kumar, Neeraj Fernando, Gayara Demini Ghosh, Souparno Sanchez-Villalobos, Cesar Zhang, Ruibo Pal, Ranadip Weil, M. Ryan Stevens, Rick L. Biomolecules Machine Learning Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community. |
| title | Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2409.12215 |