Generalized information criteria for personalized gene network inference
Identifying individual genomic characteristics is a critical focus in personalized therapies. To reveal targets in such therapies, we considered personalized gene network analysis using kernel-based L1-type regularization methods. In kernel-based L1-type regularized modeling, selecting optimal regul...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1583756/full |
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| author | Heewon Park Heewon Park Heewon Park Heewon Park Seiya Imoto Sadanori Konishi |
| author_facet | Heewon Park Heewon Park Heewon Park Heewon Park Seiya Imoto Sadanori Konishi |
| author_sort | Heewon Park |
| collection | DOAJ |
| description | Identifying individual genomic characteristics is a critical focus in personalized therapies. To reveal targets in such therapies, we considered personalized gene network analysis using kernel-based L1-type regularization methods. In kernel-based L1-type regularized modeling, selecting optimal regularization parameters is crucial because edge selection and weight estimation depend heavily on such parameters. Furthermore, selecting a kernel bandwidth that controls sample weighting is vital for personalized modeling. Although cross-validation and information criteria (i.e., AIC and BIC) are often used for parameter selection, such traditional techniques are computationally expensive or unsuitable for approaches based on estimation techniques other than maximum likelihood estimation. To overcome these issues, we introduced a novel evaluation criterion in line with the generalized information criterion (GIC), which relaxes the assumption of maximum likelihood estimation, making it suitable for personalized gene network analysis based on various estimation techniques. Monte Carlo simulations demonstrated that the proposed GIC outperforms existing evaluation criteria in terms of edge selection and weight estimation. Acute myeloid leukemia (AML) drug sensitivity-specific gene network analysis revealed critical molecular interactions to uncover ALM drugs resistant mechanism. Notably, PIK3CD activation and RARA/RELA suppression are crucial markers for improving AML chemotherapy efficacy. We also applied our strategy for gastric cancer drug sensitivity analysis and uncovered personalized therapeutic targets. We expect that the proposed sample specific GIC will be a useful tool for evaluating personalized modeling, including in sample characteristic-specific gene networks analysis. |
| format | Article |
| id | doaj-art-b03bfff41a194075ba2223c2d5c9b45f |
| institution | OA Journals |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-b03bfff41a194075ba2223c2d5c9b45f2025-08-20T02:36:59ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-06-011610.3389/fgene.2025.15837561583756Generalized information criteria for personalized gene network inferenceHeewon Park0Heewon Park1Heewon Park2Heewon Park3Seiya Imoto4Sadanori Konishi5School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul, Republic of KoreaData Science Center, Sungshin Women’s University, Seoul, Republic of KoreaHuman Genome Center, Institute of Medical Science, University of Tokyo, Bunkyo, JapanM&D Data Science Center, Institute of Science Tokyo, Tokyo, JapanHuman Genome Center, Institute of Medical Science, University of Tokyo, Bunkyo, JapanDepartment of Mathematics, Faculty of Science and Engineering, Chuo University, Hachioji, JapanIdentifying individual genomic characteristics is a critical focus in personalized therapies. To reveal targets in such therapies, we considered personalized gene network analysis using kernel-based L1-type regularization methods. In kernel-based L1-type regularized modeling, selecting optimal regularization parameters is crucial because edge selection and weight estimation depend heavily on such parameters. Furthermore, selecting a kernel bandwidth that controls sample weighting is vital for personalized modeling. Although cross-validation and information criteria (i.e., AIC and BIC) are often used for parameter selection, such traditional techniques are computationally expensive or unsuitable for approaches based on estimation techniques other than maximum likelihood estimation. To overcome these issues, we introduced a novel evaluation criterion in line with the generalized information criterion (GIC), which relaxes the assumption of maximum likelihood estimation, making it suitable for personalized gene network analysis based on various estimation techniques. Monte Carlo simulations demonstrated that the proposed GIC outperforms existing evaluation criteria in terms of edge selection and weight estimation. Acute myeloid leukemia (AML) drug sensitivity-specific gene network analysis revealed critical molecular interactions to uncover ALM drugs resistant mechanism. Notably, PIK3CD activation and RARA/RELA suppression are crucial markers for improving AML chemotherapy efficacy. We also applied our strategy for gastric cancer drug sensitivity analysis and uncovered personalized therapeutic targets. We expect that the proposed sample specific GIC will be a useful tool for evaluating personalized modeling, including in sample characteristic-specific gene networks analysis.https://www.frontiersin.org/articles/10.3389/fgene.2025.1583756/fullmodel evaluationpersonalized gene networkgeneralized information criteriaacute myeloid leukemiagastri cancer |
| spellingShingle | Heewon Park Heewon Park Heewon Park Heewon Park Seiya Imoto Sadanori Konishi Generalized information criteria for personalized gene network inference Frontiers in Genetics model evaluation personalized gene network generalized information criteria acute myeloid leukemia gastri cancer |
| title | Generalized information criteria for personalized gene network inference |
| title_full | Generalized information criteria for personalized gene network inference |
| title_fullStr | Generalized information criteria for personalized gene network inference |
| title_full_unstemmed | Generalized information criteria for personalized gene network inference |
| title_short | Generalized information criteria for personalized gene network inference |
| title_sort | generalized information criteria for personalized gene network inference |
| topic | model evaluation personalized gene network generalized information criteria acute myeloid leukemia gastri cancer |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1583756/full |
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