Multilayer Integration of Networks Toolbox (MINT)
Abstract We present MINT (Multilayer Integration of Networks Toolbox), a Python package for multimodal data integration and community detection. MINT includes data standardization, Similarity Network Fusion, Generalized Louvain clustering, visualization, cross-validation, and modality selection opti...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-08269-4 |
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| author | Saman Sarraf Bárbara Avelar-Pereira S. M. Hadi Hosseini for the Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Saman Sarraf Bárbara Avelar-Pereira S. M. Hadi Hosseini for the Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Saman Sarraf |
| collection | DOAJ |
| description | Abstract We present MINT (Multilayer Integration of Networks Toolbox), a Python package for multimodal data integration and community detection. MINT includes data standardization, Similarity Network Fusion, Generalized Louvain clustering, visualization, cross-validation, and modality selection optimization, capturing complex relationships among disease markers. We applied MINT to two multimodal datasets spanning the Alzheimer’s disease (AD) spectrum: a primary cohort of 206 participants and a validation cohort of 143 participants, including structural magnetic resonance imaging (MRI), amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), cognition, and genetics. We hypothesized that modeling intra- and inter-modality associations would improve AD prediction and identify preclinical cases. Across both datasets, MINT identified PET and CSF as optimal modalities and detected two communities: one AD-dominant and one cognitively normal-dominant (CN). Sensitivity and specificity for CN and AD were 84.38% (95% CI: 73.14–92.24) and 92.65% (95% CI: 83.67–97.57). The AD-dominant community exhibited poorer cognition and higher genetic risk and AD pathology (p < 0.001). CN individuals in this group showed elevated amyloid (p=0.009), tau (p=0.004), and ptau (p < 0.001) compared to AD individuals in the CN-dominant group. MINT can identify biologically relevant subgroups, predict disease progression, and serves as a powerful tool for uncovering complex relationships across heterogeneous and multifactorial disorders. |
| format | Article |
| id | doaj-art-df26858d759f4b1d977f9840f37d4b65 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-df26858d759f4b1d977f9840f37d4b652025-08-20T02:30:43ZengNature PortfolioCommunications Biology2399-36422025-06-018111010.1038/s42003-025-08269-4Multilayer Integration of Networks Toolbox (MINT)Saman Sarraf0Bárbara Avelar-Pereira1S. M. Hadi Hosseini2for the Alzheimer’s Disease Neuroimaging InitiativeDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Stanford UniversityDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Stanford UniversityDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Stanford UniversityAbstract We present MINT (Multilayer Integration of Networks Toolbox), a Python package for multimodal data integration and community detection. MINT includes data standardization, Similarity Network Fusion, Generalized Louvain clustering, visualization, cross-validation, and modality selection optimization, capturing complex relationships among disease markers. We applied MINT to two multimodal datasets spanning the Alzheimer’s disease (AD) spectrum: a primary cohort of 206 participants and a validation cohort of 143 participants, including structural magnetic resonance imaging (MRI), amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), cognition, and genetics. We hypothesized that modeling intra- and inter-modality associations would improve AD prediction and identify preclinical cases. Across both datasets, MINT identified PET and CSF as optimal modalities and detected two communities: one AD-dominant and one cognitively normal-dominant (CN). Sensitivity and specificity for CN and AD were 84.38% (95% CI: 73.14–92.24) and 92.65% (95% CI: 83.67–97.57). The AD-dominant community exhibited poorer cognition and higher genetic risk and AD pathology (p < 0.001). CN individuals in this group showed elevated amyloid (p=0.009), tau (p=0.004), and ptau (p < 0.001) compared to AD individuals in the CN-dominant group. MINT can identify biologically relevant subgroups, predict disease progression, and serves as a powerful tool for uncovering complex relationships across heterogeneous and multifactorial disorders.https://doi.org/10.1038/s42003-025-08269-4 |
| spellingShingle | Saman Sarraf Bárbara Avelar-Pereira S. M. Hadi Hosseini for the Alzheimer’s Disease Neuroimaging Initiative Multilayer Integration of Networks Toolbox (MINT) Communications Biology |
| title | Multilayer Integration of Networks Toolbox (MINT) |
| title_full | Multilayer Integration of Networks Toolbox (MINT) |
| title_fullStr | Multilayer Integration of Networks Toolbox (MINT) |
| title_full_unstemmed | Multilayer Integration of Networks Toolbox (MINT) |
| title_short | Multilayer Integration of Networks Toolbox (MINT) |
| title_sort | multilayer integration of networks toolbox mint |
| url | https://doi.org/10.1038/s42003-025-08269-4 |
| work_keys_str_mv | AT samansarraf multilayerintegrationofnetworkstoolboxmint AT barbaraavelarpereira multilayerintegrationofnetworkstoolboxmint AT smhadihosseini multilayerintegrationofnetworkstoolboxmint AT forthealzheimersdiseaseneuroimaginginitiative multilayerintegrationofnetworkstoolboxmint |