| Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants A Pratap, EC Neto, P Snyder, C Stepnowsky, N Elhadad, D Grant, ... NPJ digital medicine 3 (1), 21, 2020 | 255 | 2020 |
| Meta-analysis of the Alzheimer’s disease human brain transcriptome and functional dissection in mouse models YW Wan, R Al-Ouran, CG Mangleburg, TM Perumal, TV Lee, K Allison, ... Cell reports 32 (2), 2020 | 226 | 2020 |
| Detecting the impact of subject characteristics on machine learning-based diagnostic applications E Chaibub Neto, A Pratap, TM Perumal, M Tummalacherla, P Snyder, ... NPJ digital medicine 2 (1), 99, 2019 | 68 | 2019 |
| Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge SK Sieberts, J Schaff, M Duda, BÁ Pataki, M Sun, P Snyder, JF Daneault, ... NPJ digital medicine 4 (1), 53, 2021 | 48 | 2021 |
| Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson’s disease JF Daneault, G Vergara-Diaz, F Parisi, C Admati, C Alfonso, M Bertoli, ... Scientific Data 8 (1), 48, 2021 | 47 | 2021 |
| Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach BA Logsdon, TM Perumal, V Swarup, M Wang, C Funk, C Gaiteri, M Allen, ... BioRxiv, 510420, 2019 | 43 | 2019 |
| Design of a virtual longitudinal observational study in Parkinson’s disease (AT‐HOME PD) RB Schneider, L Omberg, EA Macklin, M Daeschler, L Bataille, S Anthwal, ... Annals of clinical and translational neurology 8 (2), 308-320, 2021 | 23 | 2021 |
| Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. npj Digital Medicine 2020; 3: 21 A Pratap, EC Neto, P Snyder, C Stepnowsky, N Elhadad, D Grant, ... Article PubMed PubMed Central, 2020 | 20 | 2020 |
| Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease G Vergara-Diaz, JF Daneault, F Parisi, C Admati, C Alfonso, M Bertoli, ... Scientific Data 8 (1), 47, 2021 | 16 | 2021 |
| Accelerating medicines partnership-Alzheimer’s disease consortium YW Wan, R Al-Ouran, CG Mangleburg, TM Perumal, TV Lee, K Allison, ... Carter GW, Collier DA, Golde TE, Levey AI, Bennett DA, Estrada K, Townsend …, 2020 | 13 | 2020 |
| mhealthtools: A modular r package for extracting features from mobile and wearable sensor data P Snyder, M Tummalacherla, T Perumal, L Omberg Journal of Open Source Software 5 (47), 2106, 2020 | 9 | 2020 |
| Remote assessment, in real-world setting, of tremor severity in parkinson's disease patients using smartphone inertial sensors TM Perumal, M Tummalacherla, P Snyder, EC Neto, ER Dorsey, ... Proceedings of the 2018 ACM International Joint Conference and 2018 …, 2018 | 5 | 2018 |
| Single cell tracking based on Voronoi partition via stable matching YH Chang, J Linsley, J Lamstein, J Kalra, I Epstein, M Barch, K Daily, ... 2020 59th IEEE Conference on Decision and Control (CDC), 5086-5091, 2020 | 4 | 2020 |
| Stable predictions for health related anticausal prediction tasks affected by selection biases: The need to deconfound the test set features EC Neto, P Snyder, SK Sieberts, L Omberg arXiv preprint arXiv:2011.04128, 2020 | 1 | 2020 |
| Month 24 results of a virtual longitudinal, observational study of Parkinson's disease: The AT-HOME PD cohort M Zafar, J Soto, E Baloga, L Omberg, E Macklin, P Snyder, K Amodeo, ... MOVEMENT DISORDERS 37, S361-S361, 2022 | | 2022 |
| Crowdsourcing digital health measures to predict Parkinson's disease severity SK Sieberts, J Schaff, M Duda, BÁ Pataki, M Sun, P Snyder, JF Daneault, ... | | 2021 |
| Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge Z Aydin, SK Sieberts, J Schaff, M Duda, BA Pataki, M Sun, P Snyder, ... NATURE RESEARCHHEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY, 2021 | | 2021 |
| Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features E Chaibub Neto, P Snyder, SK Sieberts, L Omberg arXiv e-prints, arXiv: 2011.04128, 2020 | | 2020 |
| Baseline results of a virtual longitudinal, observational study of Parkinson's disease: The AT-HOME PD cohort T Myers, R Schneider, L Omberg, E Macklin, E Baloga, P Snyder, ... MOVEMENT DISORDERS 35, S408-S409, 2020 | | 2020 |
| Detecting the impact of subject characteristics on machine learning-based diagnostic applications EC Neto, A Pratap, TM Perumal, M Tummalacherla, P Snyder, BM Bot, ... NPJ digital medicine 2 (1), 1-6, 2019 | | 2019 |
