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English Information

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Authors
# Name
1 Laura Galant Speggiorin(lgspeggiorin@inf.ufrgs.br)
2 Thayne Woycinck Kowalski(tkowalski@hcpa.edu.br)
3 Mariana Recamonde Mendoza(mrmendoza@inf.ufrgs.br)

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Reference
# Reference
1 Omar, K. S., Mondal, P., Khan, N. S., Rizvi, M. R. K., and Islam, M. N. (2019). A machine learning approach to predict autism spectrum disorder. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), page 1–6.
2 Hertz-Picciotto, I., Schmidt, R. J., Walker, C. K., Bennett, D. H., Oliver, M., Shedd- Wise, K. M., LaSalle, J. M., Giulivi, C., Puschner, B., Thomas, J., Roa, D. L., Pessah, I. N., Van de Water, J., Tancredi, D. J., and Ozonoff, S. (2018). A prospective study of environmental exposures and early biomarkers in autism spectrum disorder: De- sign, protocols, and preliminary data from the marbles study. Environmental Health Perspectives, 126(11):117004.
3 Sharma, S. R., Gonda, X., and Tarazi, F. I. (2018). Autism spectrum disorder: Classifica- tion, diagnosis and therapy. Pharmacology Therapeutics, 190:91–104.
4 Hodges, H., Fealko, C., and Soares, N. (2020). Autism spectrum disorder: defini- tion, epidemiology, causes, and clinical evaluation. Translational Pediatrics, 9(Suppl 1):S55–S65.
5 Tylee, D. S., Hess, J. L., Quinn, T. P., Barve, R., Huang, H., Zhang-James, Y., Chang, J., Stamova, B. S., Sharp, F. R., Hertz-Picciotto, I., Faraone, S. V., Kong, S. W., and Glatt, S. J. (2017). Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis. American journal of medical genetics. Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics, 174(3):181–201.
6 Liu, W., Li, M., and Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8):888–898.
7 Zablotsky, B., Black, L. I., and Blumberg, S. J. (2017). Estimated prevalence of children with diagnosed developmental disabilities in the united states, 2014-2016. NCHS data brief, (291):1–8.
8 Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas, G., Frazier, T., Jones, E. J. H., Jones, R. M., Pickles, A., State, M. W., Taylor, J. L., and Veenstra-VanderWeele, J. (2020). Autism spectrum disorder. Nature Reviews Disease Primers, 6(1):1–23.
9 Zhang, F., Savadjiev, P., Cai, W., Song, Y., Rathi, Y., Tunc¸, B., Parker, D., Kapur, T., Schultz, R. T., Makris, N., Verma, R., and O’Donnell, L. J. (2018). Whole brain white matter connectivity analysis using machine learning: an application to autism. NeuroImage, 172:826–837.
10 Mordaunt, C. E., Park, B. Y., Bakulski, K. M., Feinberg, J. I., Croen, L. A., Ladd-Acosta, C., Newschaffer, C. J., Volk, H. E., Ozonoff, S., Hertz-Picciotto, I., LaSalle, J. M., Schmidt, R. J., and Fallin, M. D. (2019). A meta-analysis of two high-risk prospective cohort studies reveals autism-specific transcriptional changes to chromatin, autoim- mune, and environmental response genes in umbilical cord blood. Molecular Autism, 10(1):36.
11 Newschaffer, C. J., Croen, L. A., Fallin, M. D., Hertz-Picciotto, I., Nguyen, D. V., Lee, N. L., Berry, C. A., Farzadegan, H., Hess, H. N., Landa, R. J., Levy, S. E., Massolo, M. L., Meyerer, S. C., Mohammed, S. M., Oliver, M. C., Ozonoff, S., Pandey, J., Schroeder, A., and Shedd-Wise, K. M. (2012). Infant siblings and the investigation of autism risk factors. Journal of Neurodevelopmental Disorders, 4(1):7.
12 Omar, K. S., Mondal, P., Khan, N. S., Rizvi, M. R. K., and Islam, M. N. (2019). A machine learning approach to predict autism spectrum disorder. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), page 1–6.
13 Sharma, S. R., Gonda, X., and Tarazi, F. I. (2018). Autism spectrum disorder: Classifica- tion, diagnosis and therapy. Pharmacology Therapeutics, 190:91–104.
14 Tylee, D. S., Hess, J. L., Quinn, T. P., Barve, R., Huang, H., Zhang-James, Y., Chang, J., Stamova, B. S., Sharp, F. R., Hertz-Picciotto, I., Faraone, S. V., Kong, S. W., and Glatt, S. J. (2017). Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis. American journal of medical genetics. Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics, 174(3):181–201.
15 Zablotsky, B., Black, L. I., and Blumberg, S. J. (2017). Estimated prevalence of children with diagnosed developmental disabilities in the united states, 2014-2016. NCHS data brief, (291):1–8.
16 Zhang, F., Savadjiev, P., Cai, W., Song, Y., Rathi, Y., Tunc¸, B., Parker, D., Kapur, T., Schultz, R. T., Makris, N., Verma, R., and O’Donnell, L. J. (2018). Whole brain white matter connectivity analysis using machine learning: an application to autism. NeuroImage, 172:826–837.