| Prediction of type 2 diabetes based on machine learning algorithm HM Deberneh, I Kim International journal of environmental research and public health 18 (6), 3317, 2021 | 142 | 2021 |
| Predicting output power for nearshore wave energy harvesting HM Deberneh, I Kim Applied Sciences 8 (4), 566, 2018 | 12* | 2018 |
| Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling HM Deberneh, DR Abdelrahman, SK Verma, JJ Linares, AJ Murton, ... Communications Chemistry 6 (1), 72, 2023 | 7 | 2023 |
| Software tool for visualization and validation of protein turnover rates using heavy water metabolic labeling and LC-MS HM Deberneh, RG Sadygov International journal of molecular sciences 23 (23), 14620, 2022 | 5 | 2022 |
| Pocs-based clustering algorithm LA Tran, HM Deberneh, TD Do, TD Nguyen, MH Le, DC Park 2022 International Workshop on Intelligent Systems (IWIS), 1-6, 2022 | 4 | 2022 |
| A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling HM Deberneh, DR Abdelrahman, SK Verma, JJ Linares, AJ Murton, ... Scientific Data 10 (1), 635, 2023 | 3 | 2023 |
| Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling HM Deberneh, RG Sadygov Journal of proteome research 22 (2), 410-419, 2023 | 3 | 2023 |
| 1233-P: Prediction of type 2 diabetes occurrence using machine learning model HM Deberneh, I Kim, JH Park, E Cha, KH Joung, JS Lee, DS Lim Diabetes 69 (Supplement_1), 2020 | 3 | 2020 |
| Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion RG Sadygov, JX Zhu, HM Deberneh Journal of Proteome Research, 2024 | | 2024 |
| Analytical solution for dynamic label incorporation in atom-based metabolic labeling HDRG Sadygov The 72nd ASMS Conference on Mass Spectrometry and Allied Topics (ASMS 2024 …, 2024 | | 2024 |
| Cluster analysis via projection onto convex sets LA Tran, D Kwon, HM Deberneh, DC Park Intelligent Data Analysis, 1-18, 2024 | | 2024 |
| Flexible Quality Control for Protein Turnover Rates Using d2ome HM Deberneh, RG Sadygov International journal of molecular sciences 24 (21), 15553, 2023 | | 2023 |
| The quantification of isotope enrichment from two mass isotopomers helps to reduce the proteome complexity in protein turnover estimations HMD Rovshan G. Sadygov 38th Asilomar Conference on Mass Spectrometry-Computational Mass …, 2023 | | 2023 |
| Data-driven approach to resolve precursor enrichment in metabolic labeling J Zhu, RG Sadygov , 71st American Society for Mass Spectrometry (ASMS) Conference on Mass …, 2023 | | 2023 |
| Exact Formula for Positive False Discovery Rate (pFDR) Computation JZ Rovshan G. Sadygov, HM Deberneh 71st American Society for Mass Spectrometry (ASMS) Conference on Mass …, 2023 | | 2023 |
| Resolving Proteome Complexity with Isotope Profiles for Protein Turnover Studies using heavy water metabolic labeling and LC-MS HDRG Sadygov The 19th Annual US HUPO Conference, Chicago, Illinois, March 4-8, 2023 | | 2023 |
| Generating Daily Gap-filled BRDF Adjusted Surface Reflectance Products with 10 m Resolution Using Geostationary Satellite J Kong, Y Ryu, S Jeong, W Choi, H Mamo Authorea Preprints, 2022 | | 2022 |
| Precursor enrichment in heavy water metabolic labeling Henock M Deberneh, Naveen K Ganji, Rovshan Sadygov 70th American society of mass spectrometry Conference, 2022 | | 2022 |
| Monitoring daily canopy photosynthesis using NIRvP maps at 10 m resolution using Sentinel 2 and GK2A HM Juwon Kong, Youngryel Ryu, Sungchan Jeong, Monseok Choi AsiaFlux Online Conference Proceedings,December 20-21, 2021, 2021 | | 2021 |
| Forest Analysis Ready Data (F-ARD) Songchan, Young-Ryeol Ryu, Won-Seok Choi, Henock Mamo, Jungbin Lim Agriculture and Forestry Satellite Forestry Calibration Basic Technology …, 2021 | | 2021 |
