| Tutorial: Lessons Learned for Behavior Analysts from Data Scientists L Neely, S Oyama, Q Chen, A Qutub, C Chen Perspectives on Behavior Science 47, 203–223, 2024 | | 2024 |
| Biological research and self-driving labs in deep space supported by artificial intelligence LM Sanders, RT Scott, JH Yang, AA Qutub, H Garcia Martin, DC Berrios, ... Nature Machine Intelligence 5 (3), 208-219, 2023 | 16* | 2023 |
| Biomonitoring and precision health in deep space supported by artificial intelligence RT Scott, LM Sanders, EL Antonsen, JJA Hastings, S Park, G Mackintosh, ... Nature Machine Intelligence 5 (3), 196-207, 2023 | 22* | 2023 |
| RPPA-Profiling in Pediatric and Adult T-Cell Acute Lymphoblastic Leukemia Identifies Protein Patterns Associated with Outcome FW Hoff, L Sriraja, Y Qiu, G Jenkins, A Ligeralde, DT Teachey, BL Wood, ... Blood 140 (Supplement 1), 6352-6353, 2022 | | 2022 |
| Clinical relevance of proteomic profiling in de novo pediatric acute myeloid leukemia: a Children’s Oncology Group study FW Hoff, AD van Dijk, Y Qiu, CW Hu, R E. Ries, A Ligeralde, G N. Jenkins, ... Haematologica 107 (10), 2022 | 11 | 2022 |
| Differential proteome profile, biological pathways, and network relationships of osteogenic proteins in calcified human aortic valves RI Han, CW Hu, DS Loose, L Yang, L Li, JP Connell, MJ Reardon, ... Heart and vessels, 1-12, 2022 | 4 | 2022 |
| cytoNet: Spatiotemporal network analysis of cell communities. A Mahadevan, B Long, CW Hu, DT Ryan, NE Grandel, G Britton, M Bustos, ... PLOS Comput Biol 18 (6), doi: 10.1371/journal.pcbi.1009846, 2022 | 12* | 2022 |
| Decoupling lineage-associated genes in acute myeloid leukemia reveals inflammatory and metabolic signatures associated with outcomes HA Abbas, V Mohanty, R Wang, Y Huang, S Liang, F Wang, J Zhang, ... Frontiers in Oncology 11, 705627, 2021 | 9 | 2021 |
| Integrins and extracellular matrix proteins modulate adipocyte thermogenic capacity MA Gonzalez Porras, K Stojkova, MK Vaicik, A Pelowe, A Goddi, ... Scientific reports 11 (1), 5442, 2021 | 30 | 2021 |
| Health Signatures During COVID-19: A Precision Fitness Case Study E Pollet, A Sathish, Z Maloney, B Long, J Brethen, A Qutub medRxiv preprint - https://www.medrxiv.org/content/10.1101/2020.12.07 …, 2020 | | 2020 |
| AML-211: Active Galectin 3/CD74 Axis is Associated with Poor Survival Outcome in AML P Ruvolo, YH Qiu, C Hu, V Ruvolo, N Zhang, A Qutub, M Andreeff, ... Clinical Lymphoma Myeloma and Leukemia 20, S192, 2020 | | 2020 |
| 1205 Activity Trackers As A Tool In Sleep Research: Determining Discrepancies In Trackers Vs. PSG EP Pollet, DP Pollet, B Long, AA Qutub Sleep, A460-A461, 2020 | 1 | 2020 |
| Proteomic Landscape of Acute Leukemia: A Comparison between ALL and AML in Adults FW Hoff, Y Qiu, W Hu, AA Qutub, ES de Bont, TM Horton, SM Kornblau Blood 134, 1461, 2019 | | 2019 |
| A novel self-organizing embryonic stem cell system reveals signaling logic underlying the patterning of human ectoderm G Britton, I Heemskerk, R Hodge, AA Qutub, A Warmflash Development, 2019 | 71 | 2019 |
| P2‐113: LIVING NEURAL NETWORKS: FROM CELLS TO SYSTEMS BACKGROUND AA Qutub, E Pollet, B Long, A Mahadevan, G Britton, K Francis Alzheimer's & Dementia 15, P616-P616, 2019 | | 2019 |
| TD‐P‐19: DIGITIZING BRAIN HEALTH: FROM NEURODIFFERENTIATION TO DAILY ACTIVITIES AA Qutub, E Pollet, B Long, A Mahadevan, G Britton Alzheimer's & Dementia 15, P161-P161, 2019 | | 2019 |
| Proteomic profiling of acute promyelocytic leukemia identifies two protein signatures associated with relapse FW Hoff, CW Hu, AA Qutub, Y Qiu, MJ Hornbaker, C Bueso‐Ramos, ... PROTEOMICS–Clinical Applications 13 (4), 1800133, 2019 | 8 | 2019 |
| LGALS3 is connected to CD74 in a previously unknown protein network that is associated with poor survival in patients with AML PP Ruvolo, CW Hu, Y Qiu, VR Ruvolo, RL Go, SE Hubner, KR Coombes, ... EBioMedicine 44, 126-137, 2019 | 20 | 2019 |
| Pilot Study Detecting Patterns in REM Sleep in Healthy Adults for Later Comparison with Alzheimer's Patients E Pollet, B Long, K Phelan, A Qutub SLEEP 42, 2019 | | 2019 |
| Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a Preclinical Study TT Tang, JA Zawaski, KN Francis, AA Qutub, MW Gaber Scientific Reports 9 (12529), 2019 | 51 | 2019 |
Steven KornblauU.T. M. D. Anderson Cancer Center在 mdanderson.org 的电子邮件经过验证
