| Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology B Alipanahi, F Hormozdiari, B Behsaz, J Cosentino, ZR McCaw, ... The American Journal of Human Genetics 108 (7), 1217-1230, 2021 | 81 | 2021 |
| Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models J Cosentino, B Behsaz, B Alipanahi, ZR McCaw, D Hill, TH Schwantes-An, ... Nature Genetics 55 (5), 787-795, 2023 | 73 | 2023 |
| Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction T Yun, J Cosentino, B Behsaz, ZR McCaw, D Hill, R Luben, D Lai, J Bates, ... Nature Genetics 56 (8), 1604-1613, 2024 | 43 | 2024 |
| Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer M Karimzadeh, A Momen-Roknabadi, TB Cavazos, Y Fang, NC Chen, ... Nature Communications 15 (1), 10090, 2024 | 35 | 2024 |
| On minimum vertex cover of generalized Petersen graphs B Behsaz, P Hatami, ES Mahmoodian arXiv preprint arXiv:1008.3208, 2010 | 34 | 2010 |
| Predicting cardiovascular disease risk using photoplethysmography and deep learning WH Weng, S Baur, M Daswani, C Chen, L Harrell, S Kakarmath, M Jabara, ... PLOS Global Public Health 4 (6), e0003204, 2024 | 33 | 2024 |
| On minimum sum of radii and diameters clustering B Behsaz, MR Salavatipour Algorithmica 73 (1), 143-165, 2015 | 33 | 2015 |
| Approximation Algorithms for Min-Sum k-Clustering and Balanced k-Median B Behsaz, Z Friggstad, MR Salavatipour, R Sivakumar Algorithmica 81 (3), 1006-1030, 2019 | 25* | 2019 |
| New approximation algorithms for the unsplittable capacitated facility location problem B Behsaz, MR Salavatipour, Z Svitkina Algorithmica 75 (1), 53-83, 2016 | 19 | 2016 |
| Sos 2004: An attempt towards a multi-agent rescue team SA Amraii, B Behsaz, M Izadi, H Janzadeh, F Molazem, A Rahimi, ... Proc. 8th RoboCup Int’l Symposium, 2004 | 13 | 2004 |
| A new learning algorithm for the maxq hierarchical reinforcement learning method F Mirzazadeh, B Behsaz, H Beigy 2007 International Conference on Information and Communication Technology …, 2007 | 12 | 2007 |
| Comparison of global computing with grid computing B Behsaz, P Jaferian, MR Meybodi 2006 Seventh International Conference on Parallel and Distributed Computing …, 2006 | 9 | 2006 |
| Approximation Algorithms for Minimum-Load k-Facility Location S Ahmadian, B Behsaz, Z Friggstad, A Jorati, MR Salavatipour, C Swamy ACM Transactions on Algorithms (TALG) 14 (2), 1-29, 2018 | 8 | 2018 |
| Unsupervised representation learning improves genomic discovery for lung function and respiratory disease prediction T Yun, J Cosentino, B Behsaz, ZR McCaw, D Hill, R Luben, D Lai, J Bates, ... medRxiv, 2023.04. 28.23289285, 2023 | 7* | 2023 |
| Utilizing multimodal AI to improve genetic analyses of cardiovascular traits Y Zhou, J Cosentino, T Yun, MI Biradar, J Shreibati, D Lai, ... medRxiv, 2024 | 4 | 2024 |
| NEFRL: A new neuro-fuzzy system for episodic reinforcement learning tasks B Behsaz, R Safabakhsh 2007 Frontiers in the Convergence of Bioscience and Information Technologies …, 2007 | 3 | 2007 |
| Detection of Early-Stage Colorectal Cancer Using Cell-Free oncRNA Biomarkers and Artificial Intelligence A Momen-Roknabadi, M Karimzadeh, NC Chen, TB Cavazos, J Wang, ... Clinical Cancer Research, OF1-OF10, 2025 | 2 | 2025 |
| Beyond detection: AI-based classification of breast cancer invasiveness using cell-free orphan non-coding RNAs M Karimzadeh, TB Cavazos, NC Chen, NK Tbeileh, D Siegel, ... Cancer Research 84 (6_Supplement), 3678-3678, 2024 | 2 | 2024 |
| Approximation algorithms for clustering problems B Behsaz | 2 | 2012 |
| Estimation of Probability Density Function by Dependence Tree Methods for Pattern Recognition Systems B Behsaz, M Rahmati Tech. Rep. U. Alberta, 2006 | 2 | 2006 |
Cory McLeanGoogleVerified email at google.com
