van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw. 3, 861 (2018).
Tenenbaum, J. B., Silva, V. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).
Belkin, M. & Niyogi, P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proc. 14th International Conference on Neural Information Processing Systems: Natural and Synthetic (eds Dietterich, T., Becker, S., and Ghahramani, Z.) 585–591 (Cambridge MIT Press, 2001).
Cheng, J., Liu, H., Wang, F., Li, H. & Zhu, C. Silhouette analysis for human action recognition based on supervised temporal t-SNE and incremental learning. IEEE Trans. Image Process. 24, 3203–3217 (2015).
Hajderanj, L., Weheliye, I. & Chen, D. A new supervised T-SNE with dissimilarity measure for effective data visualization and classification. In Proc. 2019 8th International Conference on Software and Information Engineering 232–236 (Association for Computing Machinery, 2019); https://doi.org/10.1145/3328833.3328853
Sainburg, T., McInnes, L. & Gentner, T. Q. Parametric UMAP embeddings for representation and semisupervised learning. Neural Comput. 33, 2881–2907 (2021).
Ribeiro, B., Vieira, A. & Carvalho das Neves, J. Supervised isomap with dissimilarity measures in embedding learning. In Progress in Pattern Recognition, Image Analysis and Applications (eds Ruiz-Shulcloper, J. & Kropatsch, W. G.) 389–396 (Springer, 2008); https://doi.org/10.1007/978-3-540-85920-8_48
de Ridder, D., Kouropteva, O., Okun, O., Pietikäinen, M. & Duin, R. P. W. Supervised locally linear embedding. In Artificial Neural Networks and Neural Information Processing (eds Kaynak, O., Alpaydin, E., Oja, E. & Xu, L.) 333–341 (Springer, 2003).
Hajderanj, L., Chen, D. & Weheliye, I. The impact of supervised manifold learning on structure preserving and classification error: a theoretical study. IEEE Access 9, 43909–43922 (2021).
Rhodes, J. S., Cutler, A. & Moon, K. R. Geometry- and accuracy-preserving random forest proximities. IEEE Trans. Pattern Anal. Mach. Intell. https://doi.org/10.1109/TPAMI.2023.3263774 (2023).
Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).
Cutler, A., Cutler, D. R. & Stevens, J. R. Random forests. In Ensemble Machine Learning: Methods and Applications (eds Zhang, C. & Ma, Y.) 157–175 (Springer, 2012); https://doi.org/10.1007/978-1-4419-9326-7_5
Duque, A. F., Wolf, G. & Moon, K. R. Visualizing high dimensional dynamical processes. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) 1–6 (IEEE, 2019); https://doi.org/10.1109/MLSP.2019.8918875
Kuchroo, M. et al. Multiscale phate identifies multimodal signatures of COVID-19. Nat. Biotechnol. 40, 681–691 (2022).
Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).
Baccin, C. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22, 38–48 (2020).
Yazar, S. et al. Single-cell eqtl mapping identifies cell type–specific genetic control of autoimmune disease. Science 376, eabf3041 (2022).
Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).
Combes, A. J. et al. Global absence and targeting of protective immune states in severe COVID-19. Nature 591, 124–130 (2021).
Kurtzke, J. F. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33, 1444–1452 (1983).
Bermel, R., Waldman, A. & Mowry, E. M. Outcome measures in multiple sclerosis. Mult. Scler. Int. 2014, 439375 (2014).
Hawkins, S. Truly benign multiple sclerosis is rare: let’s stop fooling ourselves–no. Mult. Scler. 18, 11–12 (2011).
Amato, M. P. & Portaccio, E. Truly benign multiple sclerosis is rare: let’s stop fooling ourselves–yes. Mult. Scler. 18, 13–14 (2011).
Reynders, T., D’haeseleer, M., De Keyser, J., Nagels, G. & D’hooghe, M. B. Definition, prevalence and predictive factors of benign multiple sclerosis. eNeurologicalSci 7, 37–43 (2017).
Meyer-Moock, S., Feng, Y.-S., Maeurer, M., Dippel, F.-W. & Kohlmann, T. Systematic literature review and validity evaluation of the expanded disability status scale (EDSS) and the multiple sclerosis functional composite (MSFC) in patients with multiple sclerosis. BMC Neurol. 14, 58 (2014).
Paul, F. Pathology and MRI: exploring cognitive impairment in MS. Acta Neurol. Scand. 134, 24–33 (2016).
Penner, I.-K. Evaluation of cognition and fatigue in multiple sclerosis: daily practice and future directions. Acta Neurol. Scand. 134, 19–23 (2016).
Penner, I.-K. & Paul, F. Fatigue as a symptom or comorbidity of neurological diseases. Nat. Rev. Neurol. 13, 662–675 (2017).
von Bismarck, O. et al. Treatment choices and neuropsychological symptoms of a large cohort of early MS. Neurol. Neuroimmunol. Neuroinflamm. 5, e446 (2018).
Hutchinson, M. Truly benign multiple sclerosis is rare: let’s stop fooling ourselves–commentary. Mult. Scler. 18, 15 (2011).
Confavreux, C. & Compston, A. in McAlpine’s Multiple Sclerosis 183–272 (Elsevier, 2006).
Ramsaransing, G. S. M. & De Keyser, J. Benign course in multiple sclerosis: a review. Acta Neurol. Scand. 113, 359–369 (2006).
Morrow, S. A. et al. Quantifying cognition and fatigue to enhance the sensitivity of the EDSS during relapses. Mult. Scler. J. 27, 1077–1087 (2021).
Ellenberger, D. et al. Is benign MS really benign? What a meaningful classification beyond the EDSS must take into consideration. Mult. Scler. Relat. Disord. 46, 102485 (2020).
Golan, D. et al. The association between MRI brain volumes and computerized cognitive scores of people with multiple sclerosis. Brain Cogn. 145, 105614 (2020).
Niiranen, M. et al. Grey matter atrophy in patients with benign multiple sclerosis. Brain Behav. 12, e2679 (2022).
Cree, B. A. C., Mares, J. & Hartung, H.-P. Current therapeutic landscape in multiple sclerosis: an evolving treatment paradigm. Curr. Opin. Neurol. 32, 365–377 (2019).
Smith, R., Wright, K. L. & Ashton, L. Raman spectroscopy: an evolving technique for live cell studies. Analyst 141, 3590–3600 (2016).
Zhang, W. et al. Label-free discrimination and quantitative analysis of oxidative stress induced cytotoxicity and potential protection of antioxidants using raman micro-spectroscopy and machine learning. Anal. Chim. Acta 1128, 221–230 (2020).
Fajnzylber, J. et al. SARS-CoV-2 viral load is associated with increased disease severity and mortality. Nat. Commun. 11, 5493 (2020).
Brunet-Ratnasingham, E. et al. Sustained ifn signaling is associated with delayed development of SARS-CoV-2-specific immunity. Nat. Commun. 15, 4177 (2024).
Fiorini, S. gene expression cancer RNA-Seq. UCI Machine Learning Repository https://doi.org/10.24432/C5R88H (2016).
Quan, L. et al. Most lung and colon cancer susceptibility genes are pair-wise linked in mice, humans and rats. PLoS ONE 6, e14727 (2011).
Vlachos, M. et al. Non-linear dimensionality reduction techniques for classification and visualization. In Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 645–651 (Association for Computing Machinery, 2002); https://doi.org/10.1145/775047.775143
Roweis, S. T. & Saul, L. K. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000).
Zhang, S. Enhanced supervised locally linear embedding. Pattern Recognit. Lett. 30, 1208–1218 (2009).
Balcan, M.-F., Blum, A. & Srebro, N. A theory of learning with similarity functions. Mach. Learn. 72, 89–112 (2008).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Coifman, R. R. & Lafon, S. Diffusion maps. Appl. Comput. Harmon. Anal. 21, 5–30 (2006).
Page, L., Brin, S., Motwani, R. & Winograd, T. The Pagerank Citation Ranking: bringing order to the web. in The Web Conference (IW3C2, 1999).
Anderson, E. The species problem in iris. Ann. Missouri Bot. Gard. 23, 457–509 (1936).
Kruskal, J. B. & Wish, M. Multidimensional Scaling Vol. 11 (Sage Publications, 1978).
Dexter, E., Rollwagen-Bollens, G. & Bollens, S. M. The trouble with stress: a flexible method for the evaluation of nonmetric multidimensional scaling. Limnol. Oceanogr. Methods 16, 434–443 (2018).
Shahapure, K. R. & Nicholas, C. Cluster quality analysis using silhouette score. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 747–748. IEEE, Piscataway, NJ, USA (2020). https://doi.org/10.1109/DSAA49011.2020.00096
Rhodes, J. S., Cutler, A., Wolf, G. & Moon, K. R. Random forest-based diffusion information geometry for supervised visualization and data exploration. In 2021 IEEE Statistical Signal Processing Workshop 331–335 (2021); https://doi.org/10.1109/SSP49050.2021.9513749
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Lublin, F. D. et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 83, 278–286 (2014).
Jia, Y. et al. Semi-supervised non-negative matrix factorization with dissimilarity and similarity regularization. IEEE Trans. Neural. Netw. Learn. Syst. https://doi.org/10.1109/TNNLS.2019.2933223 (2019).
Schneider, S., Lee, J. H. & Mathis, M. W. Learnable latent embeddings for joint behavioural and neural analysis. Nature 617, 360–368 (2023).
Avasarala, J. Redefining acute relapses in multiple sclerosis: Implications for phase 3 clinical trials and treatment algorithms. Innov. Clin. Neurosci. 14, 38–40 (2017).
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).
Cree, B. A. C. et al. Secondary progressive multiple sclerosis: new insights. Neurology 97, 378–388 (2021).
Polman, C. H. et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).
Berndt, D. J. & Clifford, J. Using dynamic time warping to find patterns in time series. In KDD Workshop (1994); https://api.semanticscholar.org/CorpusID:929893
Keogh, E. & Ratanamahatana, C. A. Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005).
Kruskal, J. B. & Liberman, M. The symmetric time-warp problem: From continuous to discrete. in Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison (eds Sankoff, D. & Kruskal, J. B.) 125–161 (Addison-Wesley Publishing Company, 1983).
Ratanamahatana, C. & Keogh, E. Everything you know about Dynamic Time Warping is wrong. In Third Workshop on Mining Temporal and Sequential Data, in conjunction with the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004) (ACM, 2004).
Dudani, S. A. The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6, 325–327 (1976).
Rhodes, J. S. Supervised manifold learning via random forest geometry-preserving proximities. In 14th International Conference on Sampling Theory and Applications (2023); https://openreview.net/forum?id=t6E4dZjp-e
Tremblay, K. et al. The biobanque québécoise de la COVID-19 (BQC19)—a cohort to prospectively study the clinical and biological determinants of COVID-19 clinical trajectories. PLoS ONE 16, e0245031 (2021).
Brunet-Ratnasingham, E. et al. Integrated immunovirological profiling validates plasma SARS-CoV-2 RNA as an early predictor of COVID-19 mortality. Sci. Adv. 7, eabj5629 (2021).
Prévost, J. et al. Cross-sectional evaluation of humoral responses against SARS-CoV-2 spike. Cell Rep. Med. 1, 100126 (2020).
Tang, J., Henderson, A. & Gardner, P. Exploring AdaBoost and random forests machine learning approaches for infrared pathology on unbalanced data sets. Analyst 146, 5880–5891 (2021).
Zhang, Z.-M., Chen, S. & Liang, Y.-Z. Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135, 1138–1146 (2010).
Rhodes, J. S. & Aumon, A. jakerhodes/RF-PHATE: Raman Dataset Release (RamanDataset). Zenodo https://doi.org/10.5281/zenodo.19666268 (2026).