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4 results on '"Pataki, B.Á."'

1. Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Author

Sieberts, S.K., Schaff, J., Duda, M., Pataki, B.Á., Sun, M., Snyder, P., Daneault, J.F., Parisi, F., Costante, G., Rubin, U., Banda, P., Chae, Y., Chaibub Neto, E., Dorsey, E.R., Aydın, Z., Chen, A., Elo, L.L., Espino, C., Glaab, E., Goan, E., Golabchi, F.N., Görmez, Y., Jaakkola, M.K., Jonnagaddala, J., Klén, R., Li, D., McDaniel, C., Perrin, D., Perumal, T.M., Rad, N.M., Rainaldi, E., Sapienza, S., Schwab, P., Shokhirev, N., Venäläinen, M.S., Vergara-Diaz, G., Zhang, Y., Abrami, A., Adhikary, A., Agurto, C., Bhalla, S., Bilgin, H., Caggiano, V., Cheng, J., Deng, E., Gan, Q., Girsa, R., Han, Z., Heisig, S., Huang, K., Jahandideh, S., Kopp, W., Kurz, C.F., Lichtner, G., Norel, R., Raghava, G.P.S., Sethi, T., Shawen, N., Tripathi, V., Tsai, M., Wang, T., Wu, Y., Zhang, J., Zhang, X., Wang, Y., Guan, Y., Brunner, D., Bonato, P., Mangravite, L.M., Omberg, L., AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü, Aydin, Zafer, Fonds National de la Recherche - FnR [sponsor], and Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center]

Subjects
Movement disorders, Parkinson's disease, Biotechnologie [F06] [Sciences du vivant], Neurology [D14] [Human health sciences], Medicine (miscellaneous), Disease, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], 0302 clinical medicine, Health Information Management, Evaluation methods, Biotechnology [F06] [Life sciences], Multidisciplinary, general & others [D99] [Human health sciences], 0303 health sciences, Outcome measures, Computer Science Applications, machine learning, smart sensors, bradykinesia, Biomarker (medicine), Technology Platforms, medicine.symptom, medicine.medical_specialty, Multidisciplinaire, généralités & autres [D99] [Sciences de la santé humaine], Computer applications to medicine. Medical informatics, R858-859.7, Health Informatics, Multidisciplinary, general & others [F99] [Life sciences], Digital Biomarker, Crowdsourcing, Article, VALIDATION, Parkinson’s Disease, 03 medical and health sciences, Physical medicine and rehabilitation, Machine learning, medicine, 030304 developmental biology, mobile phone, GENDER-DIFFERENCES, Neurologie [D14] [Sciences de la santé humaine], business.industry, biomarkers, medicine.disease, tremor, Digital health, nervous system diseases, Clinical trial, dyskinesia, Dyskinesia, Cardiovascular and Metabolic Diseases, HYPOTHESIS TESTS, business, Biomarkers, 030217 neurology & neurosurgery
Abstract

Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)., npj Digital Medicine, 4 (1), ISSN:2398-6352

Published
2021
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2. Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

Author

Pataki, B.Á., Matamoros, S, van der Putten, B.C.L., Remondini, D., Giampieri, E., Aytan-Aktug, D., Hendriksen, R.S., Lund, O., Csabai, I., Schultsz, C. (Constance), Pataki, B.Á., Matamoros, S, van der Putten, B.C.L., Remondini, D., Giampieri, E., Aytan-Aktug, D., Hendriksen, R.S., Lund, O., Csabai, I., and Schultsz, C. (Constance)

Published
2020
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3. Accelerating surveillance and research of antimicrobial resistance - an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences

Author

Matamoros, S. (Sébastien), Hendriksen, R.S. (Rene S.), Pataki, B.Á. (Bálint Ármin), Pakseresht, N. (Nima), Rossello, M. (Marc), Silvester, N. (Nicole), Amid, C. (Clara), Aarestrup, F.M. (Frank), Koopmans D.V.M., M.P.G. (Marion), Cochrane, G. (Guy), Csabai, I. (Istvan), Lund, O. (Ole), Schultsz, C. (Constance), The Compare Ml-Amr Group, (), Matamoros, S. (Sébastien), Hendriksen, R.S. (Rene S.), Pataki, B.Á. (Bálint Ármin), Pakseresht, N. (Nima), Rossello, M. (Marc), Silvester, N. (Nicole), Amid, C. (Clara), Aarestrup, F.M. (Frank), Koopmans D.V.M., M.P.G. (Marion), Cochrane, G. (Guy), Csabai, I. (Istvan), Lund, O. (Ole), Schultsz, C. (Constance), and The Compare Ml-Amr Group, ()

Abstract

Antimicrobial resistance (AMR) is an emerging threat to modern medicine. Improved diagnostics and surveillance of resistant bacteria require the development of next-generation analysis tools and collabora

Published
2020
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4. The COMPARE Data Hubs

Author

Amid, C. (Clara), Pakseresht, N. (Nima), Silvester, N. (Nicole), Jayathilaka, S. (Suran), Lund, O. (Ole), Dynovski, L.D. (Lukasz D.), Pataki, B.Á. (Bálint Á), Visontai, D. (Dávid), Xavier, B.B. (Basil Britto), Alako, B.T.F. (Blaise), Belka, A. (Ariane), Cisneros, J.L.B. (Jose L B), Cotten, M. (Matthew), Haringhuizen, G.B. (George B.), Harrison, P.W. (Peter W.), Höper, D. (Dirk), Holt, S. (Sam), Hundahl, C. (Camilla), Hussein, A. (Abdulrahman), Kaas, R.S. (Rolf S.), Liu, X. (Xin), Leinonen, R. (Rasko), Malhotra-Kumar, S. (Surbhi), Nieuwenhuijse, D.F. (David F.), Rahman, N. (Nadim), Dos S Ribeiro, C. (Carolina), Skiby, J.E. (Jeffrey E.), Schmitz, D. (Dennis), Stéger, J. (József), Szalai-Gindl, J.M. (János M), Thomsen, M.C.F. (Martin C F), Caccio, S. (Simone), Csabai, I. (István), Kroneman, A. (Annelies), Koopmans D.V.M., M.P.G. (Marion), Aarestrup, F.M. (Frank), Cochrane, G. (Guy), Amid, C. (Clara), Pakseresht, N. (Nima), Silvester, N. (Nicole), Jayathilaka, S. (Suran), Lund, O. (Ole), Dynovski, L.D. (Lukasz D.), Pataki, B.Á. (Bálint Á), Visontai, D. (Dávid), Xavier, B.B. (Basil Britto), Alako, B.T.F. (Blaise), Belka, A. (Ariane), Cisneros, J.L.B. (Jose L B), Cotten, M. (Matthew), Haringhuizen, G.B. (George B.), Harrison, P.W. (Peter W.), Höper, D. (Dirk), Holt, S. (Sam), Hundahl, C. (Camilla), Hussein, A. (Abdulrahman), Kaas, R.S. (Rolf S.), Liu, X. (Xin), Leinonen, R. (Rasko), Malhotra-Kumar, S. (Surbhi), Nieuwenhuijse, D.F. (David F.), Rahman, N. (Nadim), Dos S Ribeiro, C. (Carolina), Skiby, J.E. (Jeffrey E.), Schmitz, D. (Dennis), Stéger, J. (József), Szalai-Gindl, J.M. (János M), Thomsen, M.C.F. (Martin C F), Caccio, S. (Simone), Csabai, I. (István), Kroneman, A. (Annelies), Koopmans D.V.M., M.P.G. (Marion), Aarestrup, F.M. (Frank), and Cochrane, G. (Guy)

Abstract

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats, often lead to data not being shared or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

Published
2019
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