| Multi-modality machine learning predicting Parkinson’s disease MB Makarious, HL Leonard, D Vitale, H Iwaki, L Sargent, A Dadu, I Violich, ... npj Parkinson's Disease 8 (1), 35, 2022 | 65 | 2022 |
| Imputing KCs with representations of problem content and context ZA Pardos, A Dadu Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017 | 31 | 2017 |
| Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study F Faghri, F Brunn, A Dadu, A Chiò, A Calvo, C Moglia, A Canosa, ... The Lancet Digital Health 4 (5), e359-e369, 2022 | 27 | 2022 |
| dAFM: Fusing psychometric and connectionist modeling for Q-matrix refinement ZA Pardos, A Dadu Journal of Educational Data Mining 10 (2), 1-27, 2018 | 20 | 2018 |
| Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts A Dadu, V Satone, R Kaur, SH Hashemi, H Leonard, H Iwaki, ... npj Parkinson's Disease 8 (1), 172, 2022 | 18 | 2022 |
| Genetic risk factor clustering within and across neurodegenerative diseases MJ Koretsky, C Alvarado, MB Makarious, D Vitale, K Levine, ... Brain 146 (11), 4486-4494, 2023 | 14 | 2023 |
| GenoML: automated machine learning for genomics MB Makarious, HL Leonard, D Vitale, H Iwaki, D Saffo, L Sargent, A Dadu, ... arXiv preprint arXiv:2103.03221, 2021 | 14 | 2021 |
| A fully automated FAIMS-DIA proteomic pipeline for high-throughput characterization of iPSC-derived neurons L Reilly, L Peng, E Lara, D Ramos, M Fernandopulle, CB Pantazis, ... bioRxiv, 2021.11. 24.469921, 2021 | 11 | 2021 |
| Application of Aligned-UMAP to longitudinal biomedical studies A Dadu, VK Satone, R Kaur, MJ Koretsky, H Iwaki, YA Qi, DM Ramos, ... Patterns 4 (6), 2023 | 6 | 2023 |
| Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts. NPJ Parkinsons Dis. 8, 172 A Dadu, V Satone, R Kaur, SH Hashemi, H Leonard, H Iwaki, ... | 6 | 2022 |
| Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning VK Satone, R Kaur, A Dadu, H Leonard, H Iwaki, M Makarious, L Sargent, ... bioRxiv, 792432, 2019 | 6 | 2019 |
| A study of link prediction using deep learning A Dadu, A Kumar, HK Shakya, SK Arjaria, B Biswas Advanced Informatics for Computing Research: Second International Conference …, 2019 | 3 | 2019 |
| Random projections of Fischer Linear Discriminant classifier for multi-class classification I Arora, A Dadu, M Verma, KK Shukla 2016 4th International Symposium on Computational and Business Intelligence …, 2016 | 2 | 2016 |
| Multimodal Patient Representation Learning with Missing Modalities and Labels Z Wu, A Dadu, N Tustison, B Avants, M Nalls, J Sun, F Faghri The Twelfth International Conference on Learning Representations, 2023 | 1 | 2023 |
| Application of machine learning to the detection and prediction of Parkinson’s disease subtypes A Dadu University of Illinois at Urbana-Champaign, 2021 | 1 | 2021 |
| Identification and prediction of ALS subgroups using machine learning F Faghri, F Brunn, A Dadu, PARALS, ERRALS, E Zucchi, I Martinelli, ... medRxiv, 2021.04. 02.21254844, 2021 | 1 | 2021 |
| Federated learning for multi-omics: A performance evaluation in Parkinson’s disease BP Danek, MB Makarious, A Dadu, D Vitale, PS Lee, AB Singleton, ... Patterns 5 (3), 2024 | | 2024 |
| Genetic and brain imaging phenotype joint prediction of longitudinal Parkinson's Disease subtypes L Polfus, HM Shirazi, A Reardon, M Varga, A Tchourbanov, T Gosselin, ... EUROPEAN JOURNAL OF HUMAN GENETICS 32, 653-653, 2024 | | 2024 |
| ML-assisted therapeutics for neurodegenerative disorders A Dadu University of Illinois at Urbana-Champaign, 2023 | | 2023 |
| Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts. F Faghri, A Dadu, VK Satone, R Kaur, S Hashemi, H Leonard, H Iwaki, ... | | 2022 |
