Statistical testing for uncertainty of P-NET by DeepLIFT
Abstract In precision medicine, deep learning models have become essential for identifying biomarkers and predicting diagnosis and prognosis of diseases. Although these models have demonstrated considerable success in producing accurate prognostic and diagnostic predictions, their opaque decision-ma...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07156-1 |
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| _version_ | 1849434316836175872 |
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| author | Taewan Goo Chanhee Lee Sangyeon Shin Haeyoung Kim Taesung Park |
| author_facet | Taewan Goo Chanhee Lee Sangyeon Shin Haeyoung Kim Taesung Park |
| author_sort | Taewan Goo |
| collection | DOAJ |
| description | Abstract In precision medicine, deep learning models have become essential for identifying biomarkers and predicting diagnosis and prognosis of diseases. Although these models have demonstrated considerable success in producing accurate prognostic and diagnostic predictions, their opaque decision-making processes pose challenges for meaningful biological interpretation. In response, biologically informed neural networks (BINNs), such as P-NET, have been introduced to integrate biological domain knowledge. These models commonly employ measures like SHAP and DeepLIFT to determine the importance of their nodes. However, existing models are limited because they rely on importance estimates that does not consider statistical significance of these estimates. This study proposes a novel statistical testing approach designed to address these limitations. Specifically, it introduces two permutation tests–label and gene permutation test–aimed at identifying significant genes and pathways. Our findings show that this framework can measure the statistical significance of genes and pathways. Additionally, significant pathways are biologically associated with the metastasis mechanism of cancer, thereby enhancing both the interpretability and reliability of BINN-derived insights. This approach confidence in model outputs, thereby advancing the clinical applicability of BINNs in precision medicine. |
| format | Article |
| id | doaj-art-08fc32caaaf243a7be21249c075b327a |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-08fc32caaaf243a7be21249c075b327a2025-08-20T03:26:43ZengSpringerDiscover Applied Sciences3004-92612025-06-017611110.1007/s42452-025-07156-1Statistical testing for uncertainty of P-NET by DeepLIFTTaewan Goo0Chanhee Lee1Sangyeon Shin2Haeyoung Kim3Taesung Park4Interdisciplinary Program in Bioinformatics, Seoul National UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityInterdisciplinary Program in Bioinformatics, Seoul National UniversityAbstract In precision medicine, deep learning models have become essential for identifying biomarkers and predicting diagnosis and prognosis of diseases. Although these models have demonstrated considerable success in producing accurate prognostic and diagnostic predictions, their opaque decision-making processes pose challenges for meaningful biological interpretation. In response, biologically informed neural networks (BINNs), such as P-NET, have been introduced to integrate biological domain knowledge. These models commonly employ measures like SHAP and DeepLIFT to determine the importance of their nodes. However, existing models are limited because they rely on importance estimates that does not consider statistical significance of these estimates. This study proposes a novel statistical testing approach designed to address these limitations. Specifically, it introduces two permutation tests–label and gene permutation test–aimed at identifying significant genes and pathways. Our findings show that this framework can measure the statistical significance of genes and pathways. Additionally, significant pathways are biologically associated with the metastasis mechanism of cancer, thereby enhancing both the interpretability and reliability of BINN-derived insights. This approach confidence in model outputs, thereby advancing the clinical applicability of BINNs in precision medicine.https://doi.org/10.1007/s42452-025-07156-1Biologically informed neural networksP-NETExplainable AIStatistical significance test |
| spellingShingle | Taewan Goo Chanhee Lee Sangyeon Shin Haeyoung Kim Taesung Park Statistical testing for uncertainty of P-NET by DeepLIFT Discover Applied Sciences Biologically informed neural networks P-NET Explainable AI Statistical significance test |
| title | Statistical testing for uncertainty of P-NET by DeepLIFT |
| title_full | Statistical testing for uncertainty of P-NET by DeepLIFT |
| title_fullStr | Statistical testing for uncertainty of P-NET by DeepLIFT |
| title_full_unstemmed | Statistical testing for uncertainty of P-NET by DeepLIFT |
| title_short | Statistical testing for uncertainty of P-NET by DeepLIFT |
| title_sort | statistical testing for uncertainty of p net by deeplift |
| topic | Biologically informed neural networks P-NET Explainable AI Statistical significance test |
| url | https://doi.org/10.1007/s42452-025-07156-1 |
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