{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:36:18Z","timestamp":1769585778611,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T00:00:00Z","timestamp":1542672000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T00:00:00Z","timestamp":1542672000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1734853, 1636840, 1416953, 0716055 and 1023115"],"award-info":[{"award-number":["1734853, 1636840, 1416953, 0716055 and 1023115"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P20 NR015331, U54 EB020406, P50 NS091856, P30 DK089503, P30AG053760, UL1TR002240"],"award-info":[{"award-number":["P20 NR015331, U54 EB020406, P50 NS091856, P30 DK089503, P30AG053760, UL1TR002240"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s12021-018-9406-9","type":"journal-article","created":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T22:27:47Z","timestamp":1542752867000},"page":"407-421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering"],"prefix":"10.1007","volume":"17","author":[{"given":"Ming","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen A.","family":"Goutman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandr","family":"Kalinin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhramar","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfang","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3825-4375","authenticated-orcid":false,"given":"Ivo D.","family":"Dinov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,20]]},"reference":[{"issue":"3","key":"9406_CR1","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1111\/j.1467-9876.2007.00613.x","volume":"57","author":"K Abayomi","year":"2008","unstructured":"Abayomi, K., Gelman, A., & Levy, M. (2008). Diagnostics for multivariate imputations. Journal of the Royal Statistical Society: Series C (Applied Statistics), 57(3), 273\u2013291.","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"key":"9406_CR2","unstructured":"Allen-Zhu, Z., & Hazan, E. (2016). Variance reduction for faster non-convex optimization. in International Conference on Machine Learning."},{"issue":"19","key":"9406_CR3","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1212\/WNL.0000000000000951","volume":"83","author":"N Atassi","year":"2014","unstructured":"Atassi, N., Berry, J., Shui, A., Zach, N., Sherman, A., Sinani, E., Walker, J., Katsovskiy, I., Schoenfeld, D., Cudkowicz, M., & Leitner, M. (2014). The PRO-ACT database design, initial analyses, and predictive features. Neurology, 83(19), 1719\u20131725.","journal-title":"Neurology"},{"key":"9406_CR4","unstructured":"Beaulieu-Jones, B.K., & Moore, J.H. (2017). Missing data imputation in the electronic health record using deeply learned autoencoders, in Pacific Symposium on Biocomputing 2017, R.B. Altman, et al., Editors. p. 207\u2013218."},{"key":"9406_CR5","unstructured":"Bergsma, W., Croon, M.A., & Hagenaars, J.A. (2009). Marginal models: For dependent, clustered, and longitudinal categorical data. Springer Science & Business Media."},{"issue":"3\u20134","key":"9406_CR6","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1561\/2200000050","volume":"8","author":"S Bubeck","year":"2015","unstructured":"Bubeck, S. (2015). Convex optimization: Algorithms and complexity. Foundations and Trends\u00ae in Machine Learning, 8(3\u20134), 231\u2013357.","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"9406_CR7","doi-asserted-by":"crossref","unstructured":"Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of statistical software, 45(3).","DOI":"10.18637\/jss.v045.i03"},{"key":"9406_CR8","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.jbi.2015.09.021","volume":"58","author":"AV Carreiro","year":"2015","unstructured":"Carreiro, A. V., Amaral, P. M. T., Pinto, S., Tom\u00e1s, P., de Carvalho, M., & Madeira, S. C. (2015). Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in amyotrophic lateral sclerosis. Journal of biomedical informatics, 58, 133\u2013144.","journal-title":"Journal of biomedical informatics"},{"key":"9406_CR9","doi-asserted-by":"publisher","first-page":"s1","DOI":"10.1016\/S0022-510X(97)00237-2","volume":"152","author":"JM Cedarbaum","year":"1997","unstructured":"Cedarbaum, J. M., & Stambler, N. (1997). Performance of the amyotrophic lateral sclerosis functional rating scale (ALSFRS) in multicenter clinical trials. Journal of the Neurological Sciences, 152, s1\u2013s9.","journal-title":"Journal of the Neurological Sciences"},{"issue":"1","key":"9406_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/S0022-510X(99)00210-5","volume":"169","author":"JM Cedarbaum","year":"1999","unstructured":"Cedarbaum, J. M., Stambler, N., Malta, E., Fuller, C., Hilt, D., Thurmond, B., & Nakanishi, A. (1999). The ALSFRS-R: A revised ALS functional rating scale that incorporates assessments of respiratory function. Journal of the neurological sciences, 169(1), 13\u201321.","journal-title":"Journal of the neurological sciences"},{"key":"9406_CR11","unstructured":"Chatterjee, S., & Hadi, A.S. (2015). Regression analysis by example. John Wiley & Sons."},{"key":"9406_CR12","unstructured":"De Sa, J.M. (2012). Pattern recognition: concepts, methods and applications. Springer Science & Business Media."},{"issue":"1","key":"9406_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7243\/2053-7662-4-3","volume":"4","author":"ID Dinov","year":"2016","unstructured":"Dinov, I. D. (2016). Volume and value of big healthcare data. Journal of Medical Statistics and Informatics, 4(1), 1\u20137.","journal-title":"Journal of Medical Statistics and Informatics"},{"key":"9406_CR14","doi-asserted-by":"publisher","unstructured":"Dinov, I. D. (2018). Data science and predictive analytics: Biomedical and health applications using R, Springer, Computer Science, \n                    https:\/\/doi.org\/10.1007\/978-3-319-72347-1\n                    \n                  .","DOI":"10.1007\/978-3-319-72347-1"},{"issue":"8","key":"9406_CR15","doi-asserted-by":"publisher","first-page":"e0157077","DOI":"10.1371\/journal.pone.0157077","volume":"11","author":"ID Dinov","year":"2016","unstructured":"Dinov, I. D., Heavner, B., Tang, M., Glusman, G., Chard, K., Darcy, M., Madduri, R., Pa, J., Spino, C., Kesselman, C., Foster, I., Deutsch, E. W., Price, N. D., van Horn, J. D., Ames, J., Clark, K., Hood, L., Hampstead, B. M., Dauer, W., & Toga, A. W. (2016). Predictive big data analytics: A study of Parkinson's disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One, 11(8), e0157077.","journal-title":"PLoS One"},{"issue":"1","key":"9406_CR16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s12014-009-9024-5","volume":"5","author":"N Edwards","year":"2009","unstructured":"Edwards, N., Wu, X., & Tseng, C.-W. (2009). An unsupervised, model-free, machine-learning combiner for peptide identifications from tandem mass spectra. Clinical Proteomics, 5(1), 23\u201336.","journal-title":"Clinical Proteomics"},{"key":"9406_CR17","unstructured":"Fiedler, M., et al. (2006). Linear optimization problems with inexact data. Springer Science & Business Media."},{"issue":"6","key":"9406_CR18","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/S0730-725X(99)00014-4","volume":"17","author":"P Filzmoser","year":"1999","unstructured":"Filzmoser, P., Baumgartner, R., & Moser, E. (1999). A hierarchical clustering method for analyzing functional MR images. Magnetic Resonance Imaging, 17(6), 817\u2013826.","journal-title":"Magnetic Resonance Imaging"},{"issue":"12","key":"9406_CR19","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1136\/jnnp-2012-304701","volume":"84","author":"F Franchignoni","year":"2013","unstructured":"Franchignoni, F., Mora, G., Giordano, A., Volanti, P., & Chi\u00f2, A. (2013). Evidence of multidimensionality in the ALSFRS-R scale: A critical appraisal on its measurement properties using Rasch analysis. Journal of Neurology, Neurosurgery, and Psychiatry, 84(12), 1340\u20131345.","journal-title":"Journal of Neurology, Neurosurgery, and Psychiatry"},{"issue":"1\u20132","key":"9406_CR20","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3109\/21678421.2013.838970","volume":"15","author":"R Gomeni","year":"2014","unstructured":"Gomeni, R., Fava, M., & P.R.O.-A.A.C.T. Consortium. (2014). Amyotrophic lateral sclerosis disease progression model. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 15(1\u20132), 119\u2013129.","journal-title":"Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration"},{"key":"9406_CR21","unstructured":"Gong, P., et al. (2013). A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems. in International Conference on Machine Learning."},{"issue":"10","key":"9406_CR22","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1007\/s00415-010-5609-1","volume":"257","author":"PH Gordon","year":"2010","unstructured":"Gordon, P. H., Cheng, B., Salachas, F., Pradat, P. F., Bruneteau, G., Corcia, P., Lacomblez, L., & Meininger, V. (2010). Progression in ALS is not linear but is curvilinear. Journal of Neurology, 257(10), 1713\u20131717.","journal-title":"Journal of Neurology"},{"issue":"1","key":"9406_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-016-0268-5","volume":"16","author":"L Grigull","year":"2016","unstructured":"Grigull, L., et al. (2016). Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: A proof of concept multicenter prospective trial. BMC Medical Informatics and Decision Making, 16(1), 1.","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"5\u20136","key":"9406_CR24","doi-asserted-by":"publisher","first-page":"444","DOI":"10.3109\/21678421.2014.893361","volume":"15","author":"T Hothorn","year":"2014","unstructured":"Hothorn, T., & Jung, H. H. (2014). RandomForest4Life: A random Forest for predicting ALS disease progression. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 15(5\u20136), 444\u2013452.","journal-title":"Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration"},{"issue":"12","key":"9406_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005887","volume":"13","author":"Z Huang","year":"2017","unstructured":"Huang, Z., Zhang, H., Boss, J., Goutman, S. A., Mukherjee, B., Dinov, I. D., Guan, Y., & for the Pooled Resource Open-Access ALS Clinical Trials Consortium. (2017). Complete hazard ranking to analyze right-censored data: An ALS survival study. PLOS Computational Biology, 13(12), e1005887.","journal-title":"PLOS Computational Biology"},{"issue":"8","key":"9406_CR26","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition letters, 31(8), 651\u2013666.","journal-title":"Pattern Recognition letters"},{"issue":"3\u20134","key":"9406_CR27","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1561\/2200000058","volume":"10","author":"P Jain","year":"2017","unstructured":"Jain, P., & Kar, P. (2017). Non-convex optimization for machine learning. Foundations and Trends\u00ae in Machine Learning, 10(3\u20134), 142\u2013336.","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"issue":"12","key":"9406_CR28","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1109\/42.819322","volume":"18","author":"C Kai-Hsiang","year":"1999","unstructured":"Kai-Hsiang, C., et al. (1999). Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means. IEEE Transactions on Medical Imaging, 18(12), 1117\u20131128.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"9406_CR29","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/nbt.3051","volume":"33","author":"R Kuffner","year":"2015","unstructured":"Kuffner, R., et al. (2015). Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nature Biotechnology, 33(1), 51\u201357.","journal-title":"Nature Biotechnology"},{"key":"9406_CR30","unstructured":"Maaten, L.v.d., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579\u20132605."},{"issue":"2","key":"9406_CR31","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1137\/140957639","volume":"25","author":"J Mairal","year":"2015","unstructured":"Mairal, J. (2015). Incremental majorization-minimization optimization with application to large-scale machine learning. SIAM Journal on Optimization, 25(2), 829\u2013855.","journal-title":"SIAM Journal on Optimization"},{"issue":"4","key":"9406_CR32","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S., Poewe, W., Mollenhauer, B., Klinik, P. E., Sherer, T., Frasier, M., Meunier, C., Rudolph, A., Casaceli, C., Seibyl, J., Mendick, S., Schuff, N., Zhang, Y., Toga, A., Crawford, K., Ansbach, A., de Blasio, P., Piovella, M., Trojanowski, J., Shaw, L., Singleton, A., Hawkins, K., Eberling, J., Brooks, D., Russell, D., Leary, L., Factor, S., Sommerfeld, B., Hogarth, P., Pighetti, E., Williams, K., Standaert, D., Guthrie, S., Hauser, R., Delgado, H., Jankovic, J., Hunter, C., Stern, M., Tran, B., Leverenz, J., Baca, M., Frank, S., Thomas, C. A., Richard, I., Deeley, C., Rees, L., Sprenger, F., Lang, E., Shill, H., Obradov, S., Fernandez, H., Winters, A., Berg, D., Gauss, K., Galasko, D., Fontaine, D., Mari, Z., Gerstenhaber, M., Brooks, D., Malloy, S., Barone, P., Longo, K., Comery, T., Ravina, B., Grachev, I., Gallagher, K., Collins, M., Widnell, K. L., Ostrowizki, S., Fontoura, P., Ho, T., Luthman, J., Brug, M. . ., Reith, A. D., & Taylor, P. (2011). The Parkinson progression marker initiative (PPMI). Progress in Neurobiology, 95(4), 629\u2013635.","journal-title":"Progress in Neurobiology"},{"issue":"3","key":"9406_CR33","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1080\/10705511.2012.687667","volume":"19","author":"KA Markus","year":"2012","unstructured":"Markus, K. A. (2012). Principles and practice of structural equation modeling by Rex B. Kline. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 509\u2013512.","journal-title":"Structural Equation Modeling: A Multidisciplinary Journal"},{"issue":"4","key":"9406_CR34","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.3233\/JAD-150335","volume":"48","author":"SW Moon","year":"2015","unstructured":"Moon, S. W., et al. (2015a). Structural neuroimaging genetics interactions in Alzheimer\u2019s disease. Journal of Alzheimer's Disease, 48(4), 1051\u20131063.","journal-title":"Journal of Alzheimer's Disease"},{"issue":"5","key":"9406_CR35","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1111\/jon.12252","volume":"25","author":"SW Moon","year":"2015","unstructured":"Moon, S. W., Dinov, I. D., Hobel, S., Zamanyan, A., Choi, Y. C., Shi, R., Thompson, P. M., Toga, A. W., & for the Alzheimer's Disease Neuroimaging Initiative. (2015b). Structural brain changes in early-onset Alzheimer's disease subjects using the LONI pipeline environment. Journal of Neuroimaging, 25(5), 728\u2013737.","journal-title":"Journal of Neuroimaging"},{"issue":"4","key":"9406_CR36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174925","volume":"12","author":"M-L Ong","year":"2017","unstructured":"Ong, M.-L., Tan, P. F., & Holbrook, J. D. (2017). Predicting functional decline and survival in amyotrophic lateral sclerosis. PLoS One, 12(4), e0174925.","journal-title":"PLoS One"},{"key":"9406_CR37","doi-asserted-by":"crossref","unstructured":"Pfohl, S. R., Kim, R. B., Coan, G. S., & Mitchell, C. S. (2018). Unraveling the complexity of amyotrophic lateral sclerosis survival prediction. Frontiers in Neuroinformatics, 12(36).","DOI":"10.3389\/fninf.2018.00036"},{"key":"9406_CR38","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","volume":"67","author":"V Rodriguez-Galiano","year":"2012","unstructured":"Rodriguez-Galiano, V., et al. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93\u2013104.","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"1","key":"9406_CR39","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1061\/(ASCE)CP.1943-5487.0000003","volume":"24","author":"S Saitta","year":"2010","unstructured":"Saitta, S., Kripakaran, P., Raphael, B., & Smith, I. F. C. (2010). Feature selection using stochastic search: An application to system identification. Journal of Computing in Civil Engineering, 24(1), 3\u201310.","journal-title":"Journal of Computing in Civil Engineering"},{"issue":"7","key":"9406_CR40","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.jalz.2015.05.009","volume":"11","author":"AJ Saykin","year":"2015","unstructured":"Saykin, A. J., Shen, L., Yao, X., Kim, S., Nho, K., Risacher, S. L., Ramanan, V. K., Foroud, T. M., Faber, K. M., Sarwar, N., Munsie, L. M., Hu, X., Soares, H. D., Potkin, S. G., Thompson, P. M., Kauwe, J. S., Kaddurah-Daouk, R., Green, R. C., Toga, A. W., Weiner, M. W., & Alzheimer's Disease Neuroimaging Initiative. (2015). Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans. Alzheimers & Dementia, 11(7), 792\u2013814.","journal-title":"Alzheimers & Dementia"},{"key":"9406_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1201\/9781420089653.ch10","volume":"9","author":"D Steinberg","year":"2009","unstructured":"Steinberg, D., & Colla, P. (2009). Cart: classification and regression trees. The Top Ten Algorithms in Data Mining, 9, 179.","journal-title":"The Top Ten Algorithms in Data Mining"},{"issue":"2","key":"9406_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i02","volume":"45","author":"Y-S Su","year":"2011","unstructured":"Su, Y.-S., et al. (2011). Multiple imputation with diagnostics (mi) in R: Opening windows into the black box. Journal of Statistical Software, 45(2), 1\u201331.","journal-title":"Journal of Statistical Software"},{"issue":"4","key":"9406_CR43","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.1016\/j.neuroimage.2007.09.070","volume":"39","author":"Z Tam\u00e1s Kincses","year":"2008","unstructured":"Tam\u00e1s Kincses, Z., Johansen-Berg, H., Tomassini, V., Bosnell, R., Matthews, P. M., & Beckmann, C. F. (2008). Model-free characterization of brain functional networks for motor sequence learning using fMRI. NeuroImage, 39(4), 1950\u20131958.","journal-title":"NeuroImage"},{"issue":"11","key":"9406_CR44","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1002\/acn3.348","volume":"3","author":"AA Taylor","year":"2016","unstructured":"Taylor, A. A., Fournier, C., Polak, M., Wang, L., Zach, N., Keymer, M., Glass, J. D., Ennist, D. L., & The Pooled Resource Open-Access ALS Clinical Trials Consortium. (2016). Predicting disease progression in amyotrophic lateral sclerosis. Annals of Clinical and Translational Neurology, 3(11), 866\u2013875.","journal-title":"Annals of Clinical and Translational Neurology"},{"issue":"5","key":"9406_CR45","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/S1474-4422(18)30089-9","volume":"17","author":"H-J Westeneng","year":"2018","unstructured":"Westeneng, H.-J., Debray, T. P. A., Visser, A. E., van Eijk, R. P. A., Rooney, J. P. K., Calvo, A., Martin, S., McDermott, C. J., Thompson, A. G., Pinto, S., Kobeleva, X., Rosenbohm, A., Stubendorff, B., Sommer, H., Middelkoop, B. M., Dekker, A. M., van Vugt, J. J. F. A., van Rheenen, W., Vajda, A., Heverin, M., Kazoka, M., Hollinger, H., Gromicho, M., K\u00f6rner, S., Ringer, T. M., R\u00f6diger, A., Gunkel, A., Shaw, C. E., Bredenoord, A. L., van Es, M. A., Corcia, P., Couratier, P., Weber, M., Grosskreutz, J., Ludolph, A. C., Petri, S., de Carvalho, M., van Damme, P., Talbot, K., Turner, M. R., Shaw, P. J., al-Chalabi, A., Chi\u00f2, A., Hardiman, O., Moons, K. G. M., Veldink, J. H., & van den Berg, L. H. (2018). Prognosis for patients with amyotrophic lateral sclerosis: Development and validation of a personalised prediction model. The Lancet Neurology, 17(5), 423\u2013433.","journal-title":"The Lancet Neurology"},{"issue":"1","key":"9406_CR46","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.jbi.2003.12.002","volume":"37","author":"A Wism\u00fcller","year":"2004","unstructured":"Wism\u00fcller, A., Meyer-B\u00e4se, A., Lange, O., Auer, D., Reiser, M. F., & Sumners, D. W. (2004). Model-free functional MRI analysis based on unsupervised clustering. Journal of Biomedical Informatics, 37(1), 10\u201318.","journal-title":"Journal of Biomedical Informatics"},{"key":"9406_CR47","doi-asserted-by":"crossref","unstructured":"Wistuba, M., Schilling, N., & Schmidt-Thieme, L.. (2015). Sequential model-free Hyperparameter tuning. in Data mining (ICDM), 2015 IEEE International Conference on.","DOI":"10.1109\/ICDM.2015.20"},{"key":"9406_CR48","unstructured":"Witten, I.H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann."},{"issue":"2","key":"9406_CR49","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s13311-015-0336-z","volume":"12","author":"N Zach","year":"2015","unstructured":"Zach, N., Ennist, D. L., Taylor, A. A., Alon, H., Sherman, A., Kueffner, R., Walker, J., Sinani, E., Katsovskiy, I., Cudkowicz, M., & Leitner, M. L. (2015). Being PRO-ACTive: What can a clinical trial database reveal about ALS? Neurotherapeutics, 12(2), 417\u2013423.","journal-title":"Neurotherapeutics"},{"issue":"4","key":"9406_CR50","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/5326.897072","volume":"30","author":"GP Zhang","year":"2000","unstructured":"Zhang, G. P. (2000). Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(4), 451\u2013462.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-018-9406-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12021-018-9406-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-018-9406-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,17]],"date-time":"2020-05-17T16:17:49Z","timestamp":1589732269000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12021-018-9406-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,20]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["9406"],"URL":"https:\/\/doi.org\/10.1007\/s12021-018-9406-9","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,20]]},"assertion":[{"value":"20 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"University of Michigan Institutional Review Board (IRB) approval (HUM00115107) was obtained prior to managing, processing and analyzing the PRO-ACT data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"S.A.G. Dr. Goutman has received research support from the NIH\/NIEHS (K23ES027221), Agency for Toxic Substances and Disease Registry\/Centers for Disease Control, the ALS Association, Target ALS, Cytokinetics, and Neuralstem, Inc., and consulted for Cytokinetics.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}