mlni


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Version 0.1.4 PyPI version JSON
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home_pagehttps://github.com/anbai106/mlni
SummaryMachine Learning in NeuroImaging for various tasks, e.g., regression, classification and clustering.
upload_time2023-09-24 15:00:46
maintainer
docs_urlNone
authorjunhao.wen
requires_python
license
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requirements No requirements were recorded.
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coveralls test coverage No coveralls. 
            <h1 align="center">
  <a href="https://anbai106.github.io/mlni/">
    <img src="https://anbai106.github.io/mlni/images/mlni.png" alt="mlni Logo">
  </a>
  <br/>
  MLNI
</h1>

<p align="center"><strong>Machine Learning in NeuroImaging</strong></p>

<p align="center">
  <a href="https://anbai106.github.io/mlni/">Documentation</a>
</p>

## `MLNI`
MLNI is a python package that performs various tasks using neuroimaging data: i) binary classification for disease diagnosis, following good practice proposed in [AD-ML](https://github.com/aramis-lab/AD-ML); ii) regression prediction, such as age prediction; and iii) semi-supervised clustering with [HYDRA](https://github.com/evarol/HYDRA).

> :warning: **The documentation of this software is currently under development**

## Citing this work
### If you use this software for clustering:
> Varol, E., Sotiras, A., Davatzikos, C., 2017. **HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework**. Neuroimage, 145, pp.346-364. [doi:10.1016/j.neuroimage.2016.02.041](https://www.sciencedirect.com/science/article/abs/pii/S1053811916001506?via%3Dihub) - [Paper in PDF](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408358/pdf/nihms762663.pdf)

### If you use this software for classification or regression:
> Wen, J., Samper-González, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Epelbaum, S., Bertrand, A. and Colliot, O., 2020. **Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease**. Neuroinformatics, pp.1-22. [doi:10.1007/s12021-020-09469-5](https://link.springer.com/article/10.1007/s12021-020-09469-5) - [Paper in PDF](https://arxiv.org/abs/1812.11183)

> J. Samper-Gonzalez, N. Burgos, S. Bottani, S. Fontanella, P. Lu, A. Marcoux, A. Routier, J. Guillon, M. Bacci, J. Wen, A. Bertrand, H. Bertin, M.-O. Habert, S. Durrleman, T. Evgeniou and O. Colliot, **Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data**. NeuroImage, 183:504–521, 2018 [doi:10.1016/j.neuroimage.2018.08.042](https://doi.org/10.1016/j.neuroimage.2018.08.042) - [Paper in PDF](https://hal.inria.fr/hal-01858384/document) - [Supplementary material](https://hal.inria.fr/hal-01858384/file/supplementary_data.xlsx)

## Publication using MLNI
> Wen, J., Varol, E., Davatzikos, C., 2020. **Multi-scale feature reduction and semi-supervised learning for parsing neuroanatomical heterogeneity**. Organization for Human Brain Mapping. - [Link](https://www.researchgate.net/publication/346965816_Multi-scale_feature_reduction_and_semi-supervised_learning_for_parsing_neuroanatomical_heterogeneity)

> Wen, J., Varol, E., Davatzikos, C., 2021. **Multi-scale semi-supervised clustering of brain images: deriving disease subtypes**. MedIA. - [Link](https://www.sciencedirect.com/science/article/abs/pii/S1361841521003492)

> Wen, J., Fu, C.H., Tosun, Davatzikos, C. 2022. **Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression**. JAMA Psychiatry -  [Link](https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2789902)

> Lalousis, P.A., Schmaal, L., Wood, S.J., Reniers, R.L., Barnes, N.M., Chisholm, K., Griffiths, S.L., Stainton, A., Wen, J., Hwang, G. and Davatzikos, C., 2022. **Neurobiologically Based Stratification of Recent Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes**. Biological Psychiatry. -  [Link](https://www.sciencedirect.com/science/article/pii/S0006322322011568#bib50)

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junhao.wen
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