@article{shapley1951notes,
  title={Notes on the n-person game—ii: The value of an n-person game.(1951)},
  author={Shapley, Lloyd S},
  journal={Lloyd S Shapley},
  year={1951}
}

@software{BortuaShap,
  author       = {Eoghan Keany},
  title        = {{BorutaShap : A wrapper feature selection method 
                   which combines the Boruta feature selection
                   algorithm with Shapley values.}},
  month        = nov,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.4247618},
  url          = {https://doi.org/10.5281/zenodo.4247618}
}

@article{JSSv036i11,
 title={Feature Selection with the Boruta Package},
 volume={36},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v036i11},
 doi={10.18637/jss.v036.i11},
 abstract={This article describes a &amp;lt;b&amp;gt;R&amp;lt;/b&amp;gt; package &amp;lt;b&amp;gt;Boruta&amp;lt;/b&amp;gt;, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The &amp;lt;b&amp;gt;Boruta&amp;lt;/b&amp;gt; package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.},
 number={11},
 journal={Journal of Statistical Software},
 author={Kursa, Miron B. and Rudnicki, Witold R.},
 year={2010},
 pages={1–13}
}

@article{NoiseR,
 title={noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise},
 volume={49},
 number={14},
 doi={10.1093/nar/gkab433},
 journal={Nucleic Acids Research},
 author={Moutsopoulos, Ilias. and Maischak, Lukas and Lauzikaite, Elze and Vasquez Urbina, Sergio A and Williams, Eleanor C and Drost, Hajk-Georg and Mohorianu, Irina I},
 year={2021},
 pages={e83}
}

@article{ETC,
 title={Extremely randomized trees},
 volume={63},
 number={1},
 doi={10.1007/s10994-006-6226-1},
 journal={Machine Learning},
 author={Geurts, Pierre and Ernst, Damien and Wehenkel, Louis},
 year={2006},
 pages={3-42}
}

@article{MLVir,
 title={Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation},
 volume={13},
 number={1},
 doi={10.1038/s41467-022-32746-7},
 journal={Nature Communications},
 author={Rasmussen, Sophie Lund and Pertoldi, Cino and Macdonald, David W.},
 year={2022},
 pages={5101}
}

@article{lowabdmicrobiome,
    doi = {10.1371/journal.pcbi.1005361},
    author = {Claussen, Jens Christian AND Skiecevičienė, Jurgita AND Wang, Jun AND Rausch, Philipp AND Karlsen, Tom H. AND Lieb, Wolfgang AND Baines, John F. AND Franke, Andre AND Hütt, Marc-Thorsten},
    journal = {PLOS Computational Biology},
    publisher = {Public Library of Science},
    title = {Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome},
    year = {2017},
    month = {06},
    volume = {13},
    url = {https://doi.org/10.1371/journal.pcbi.1005361},
    pages = {1-21},
    abstract = {The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.},
    number = {6},
}

@article{CD,
 title={A comparative study of the gut microbiota in immune-mediated inflammatory diseases—does a common dysbiosis exist?},
 volume={6},
 url={https://doi.org/10.1186/s40168-018-0603-4},
 doi={10.1186/s40168-018-0603-4},
 abstract={Immune-mediated inflammatory disease (IMID) represents a substantial health concern. It is widely recognized that IMID patients are at a higher risk for developing secondary inflammation-related conditions. While an ambiguous etiology is common to all IMIDs, in recent years, considerable knowledge has emerged regarding the plausible role of the gut microbiome in IMIDs. This study used 16S rRNA gene amplicon sequencing to compare the gut microbiota of patients with Crohn’s disease (CD; N = 20), ulcerative colitis (UC; N = 19), multiple sclerosis (MS; N = 19), and rheumatoid arthritis (RA; N = 21) versus healthy controls (HC; N = 23). Biological replicates were collected from participants within a 2-month interval. This study aimed to identify common (or unique) taxonomic biomarkers of IMIDs using both differential abundance testing and a machine learning approach.},
 number={1},
 journal={Microbiome},
 author={Forbes, Jessica D. and Chen, Chih-yu and Knox, Natalie C. and Marrie, Ruth-Ann and El-Gabalawy, Hani and de Kievit, Teresa and Alfa, Michelle and Bernstein, Charles N. and Van Domselaar, Gary},
 year={2018},
 pages={221}
}

@Inbook{Thanh-Hai2021,
author={Thanh-Hai, Nguyen
and Tran, Toan Bao
and Phan, Nhi Yen Kim
and Dien, Tran Thanh
and Thai-Nghe, Nguyen},
editor={Phuong, Nguyen Hoang
and Kreinovich, Vladik},
title={Feature Selection Based on Shapley Additive Explanations on Metagenomic Data for Colorectal Cancer Diagnosis},
bookTitle={Soft Computing: Biomedical and Related Applications},
year={2021},
publisher={Springer International Publishing},
pages={69--80},
abstract={Personalized medicine is one of the hottest current approaches to take care of and improve human health. Scientists who participate in projects related to personalized medicine approaches usually consider metagenomic data as a valuable data source for developing and proposing methods for disease treatments. We usually face challenges for processing metagenomic data because of its high dimensionality and complexities. Numerous studies have attempted to find biomarkers which can be medical signs related significantly to the diseases. In this study, we propose an approach based on Shapley Additive Explanations, a model explainability, to select valuable features from metagenomic data to improve the disease prediction tasks. The proposed feature selection method is evaluated on more than 500 samples of colorectal cancer coming from various geographic regions such as France, China, the United States, Austria, and Germany. The set of 10 selected features based on Shapley Additive Explanations can achieve significant results compared to the feature selection method based on the Pearson coefficient and it also obtains comparative performances compared to the original set of features including approximately 2000 features.},
isbn={978-3-030-76620-7},
doi={10.1007/978-3-030-76620-7_6},
url={https://doi.org/10.1007/978-3-030-76620-7_6}
}

@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}

@article{SHAP1,
  title={From local explanations to global understanding with explainable AI for trees},
  author={Lundberg, Scott M. and Erion, Gabriel and Chen, Hugh and DeGrave, Alex and Prutkin, Jordan M. and Nair, Bala and Katz, Ronit and Himmelfarb, Jonathan and Bansal, Nisha and Lee, Su-In},
  journal={Nature Machine Intelligence},
  volume={2},
  number={1},
  pages={2522-5839},
  year={2020},
  publisher={Nature Publishing Group},
  doi={10.1038/s42256-019-0138-9}
}

@inproceedings{SHAP2,
author = {Lundberg, Scott M. and Lee, Su-In},
title = {A Unified Approach to Interpreting Model Predictions},
year = {2017},
isbn = {9781510860964},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.},
booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
pages = {4768–4777},
numpages = {10},
location = {Long Beach, California, USA},
series = {NIPS'17},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf},
}

@article{wilcoxon,
 ISSN = {00994987},
 URL = {http://www.jstor.org/stable/3001968},
 author = {Frank Wilcoxon},
 journal = {Biometrics Bulletin},
 number = {6},
 pages = {80--83},
 publisher = {[International Biometric Society, Wiley]},
 title = {Individual Comparisons by Ranking Methods},
 urldate = {2023-05-02},
 volume = {1},
 year = {1945},
 doi = {10.2307/3001968}
}

@article{LANDMark,
  title={LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data},
  author={Rudar, Josip and Porter, Teresita M. and Wright, Michael and Golding, G. Brian and Hajibabaei, Mehrdad},
  journal={BMC Bioinformatics},
  volume={23},
  number={1},
  year={2022},
  doi={10.1186/s12859-022-04631-z}
}

@article{TreeOrdination,
author = {Josip Rudar and G. Brian Golding and Stefan C. Kremer and Mehrdad Hajibabaei},
title = {Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data},
journal = {Microbiology Spectrum},
volume = {11},
number = {2},
pages = {e02065-22},
year = {2023},
doi = {10.1128/spectrum.02065-22},
URL = {https://journals.asm.org/doi/abs/10.1128/spectrum.02065-22},
eprint = {https://journals.asm.org/doi/pdf/10.1128/spectrum.02065-22},
abstract = { There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn’s disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn’s disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set. IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer. }
}

@INPROCEEDINGS{SingleCell,
  author={Walsh, Evan S. and Ghashghaei, H. Troy and Peng, Xinxia},
  booktitle={2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, 
  title={Feature selection using co-occurrence correlation improves cell clustering and embedding in single cell RNAseq data}, 
  year={2021},
  volume={},
  number={},
  pages={751-756},
  doi={10.1109/BIBM52615.2021.9669819}
}

@inproceedings{SAGE,
author = {Covert, Ian C. and Lundberg, Scott and Lee, Su-In},
title = {Understanding Global Feature Contributions with Additive Importance Measures},
year = {2020},
isbn = {9781713829546},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {Understanding the inner workings of complex machine learning models is a longstanding problem and most recent research has focused on local interpretability. To assess the role of individual input features in a global sense, we explore the perspective of defining feature importance through the predictive power associated with each feature. We introduce two notions of predictive power (model-based and universal) and formalize this approach with a framework of additive importance measures, which unifies numerous methods in the literature. We then propose SAGE, a model-agnostic method that quantifies predictive power while accounting for feature interactions. Our experiments show that SAGE can be calculated efficiently and that it assigns more accurate importance values than other methods.},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
articleno = {1444},
numpages = {12},
location = {Vancouver, BC, Canada},
series = {NIPS'20}
}

@article{Stability,
  author  = {Nogueira, Sarah and Sechidis, Konstantinos and Brown, Gavin},
  title   = {On the Stability of Feature Selection Algorithms},
  journal = {Journal of Machine Learning Research},
  year    = {2018},
  volume  = {18},
  number  = {174},
  pages   = {1--54},
  url     = {http://jmlr.org/papers/v18/17-514.html}
}
