Evolution of neoantigen landscape during immune checkpoint blockade in non–small cell lung cancer V Anagnostou, KN Smith, PM Forde, N Niknafs, R Bhattacharya, J White, ... Cancer discovery 7 (3), 264-276, 2017 | 869 | 2017 |
High grade serous ovarian carcinomas originate in the fallopian tube SI Labidi-Galy, E Papp, D Hallberg, N Niknafs, V Adleff, M Noe, ... Nature communications 8 (1), 1093, 2017 | 710 | 2017 |
Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure C Tokheim, R Bhattacharya, N Niknafs, DM Gygax, R Kim, M Ryan, ... Cancer research 76 (13), 3719-3731, 2016 | 145 | 2016 |
High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets XM Shao, R Bhattacharya, J Huang, IK Sivakumar, C Tokheim, L Zheng, ... Cancer immunology research 8 (3), 396-408, 2020 | 126 | 2020 |
Causal Inference Under Interference And Network Uncertainty R Bhattacharya, D Malinsky, I Shpitser Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence …, 2019 | 68 | 2019 |
Differentiable causal discovery under unmeasured confounding R Bhattacharya, T Nagarajan, D Malinsky, I Shpitser International Conference on Artificial Intelligence and Statistics, 2314-2322, 2021 | 66 | 2021 |
Semiparametric inference for causal effects in graphical models with hidden variables R Bhattacharya, R Nabi, I Shpitser Journal of Machine Learning Research 23 (295), 1-76, 2022 | 63 | 2022 |
CRAVAT 4: cancer-related analysis of variants toolkit DL Masica, C Douville, C Tokheim, R Bhattacharya, RG Kim, K Moad, ... Cancer research 77 (21), e35-e38, 2017 | 63 | 2017 |
Full law identification in graphical models of missing data: Completeness results R Nabi, R Bhattacharya, I Shpitser International conference on machine learning, 7153-7163, 2020 | 52 | 2020 |
Identification In Missing Data Models Represented By Directed Acyclic Graphs R Bhattacharya, R Nabi, I Shpitser, JM Robins Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence …, 2019 | 41 | 2019 |
Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins R Bhattacharya, A Sivakumar, C Tokheim, VB Guthrie, V Anagnostou, ... BioRxiv, 154757, 2017 | 41 | 2017 |
Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 2017; 7 (3): 264–276. doi: 10.1158/2159-8290 V Anagnostou, KN Smith, PM Forde, N Niknafs, R Bhattacharya, J White, ... CD-16-0828, 0 | 35 | |
Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges B Cai, B Li, N Kiga, J Thusberg, T Bergquist, YC Chen, N Niknafs, ... Human mutation 38 (9), 1266-1276, 2017 | 18 | 2017 |
Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 2017; 7: 264–76 V Anagnostou, KN Smith, PM Forde, N Niknafs, R Bhattacharya, J White, ... | 13 | |
An mHealth App (Speech Banana) for auditory training: app design and development study JT Ratnanather, R Bhattacharya, MB Heston, J Song, LR Fernandez, ... JMIR mHealth and uHealth 9 (3), e20890, 2021 | 11 | 2021 |
On Testability of the Front-Door Model via Verma Constraints R Bhattacharya, R Nabi Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence …, 2022 | 10 | 2022 |
Prediction of peptide binding to MHC Class I proteins in the age of deep learning R Bhattacharya, C Tokheim, A Sivakumar, VB Guthrie, V Anagnostou, ... bioRxiv, 154757, 2017 | 9 | 2017 |
Causal and counterfactual views of missing data models R Nabi, R Bhattacharya, I Shpitser, J Robins arXiv preprint arXiv:2210.05558, 2022 | 8 | 2022 |
On testability and goodness of fit tests in missing data models R Nabi, R Bhattacharya Uncertainty in Artificial Intelligence, 1467-1477, 2023 | 5 | 2023 |
Ananke: A python package for causal inference using graphical models JJR Lee, R Bhattacharya, R Nabi, I Shpitser arXiv preprint arXiv:2301.11477, 2023 | 5 | 2023 |