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Nathan Gaw - Publications

Affiliations: 
2019 Arizona State University, Tempe, AZ, United States 
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9 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2021 Yoon H, Gaw N. A novel multi-task linear mixed model for smartphone-based telemonitoring Expert Systems With Applications. 164: 113809. DOI: 10.1016/J.Eswa.2020.113809  0.573
2019 Gaw N, Hawkins-Daarud A, Hu LS, Yoon H, Wang L, Xu Y, Jackson PR, Singleton KW, Baxter LC, Eschbacher J, Gonzales A, Nespodzany A, Smith K, Nakaji P, Mitchell JR, et al. Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Scientific Reports. 9: 10063. PMID 31296889 DOI: 10.1038/S41598-019-46296-4  0.615
2018 Hu L, Gaw N, Yoon H, Eschbacher J, C. Baxter L, A. Smith K, Nakaji P, P. Karis J, Whitmire P, Hawkins-Daarud A, Singleton K, Jackson P, Christine Massey S, Bendok B, Mitchell J, et al. NIMG-12. RADIOGENOMICS ON VENUS AND MARS: IMPACT OF SEX-DIFFERENCES ON MRI AND GENETIC CORRELATIONS IN GLIOBLASTOMA Neuro-Oncology. 20: vi178-vi178. DOI: 10.1093/Neuonc/Noy148.739  0.577
2017 Jackson P, Gaw N, Hawkins-Daarud A, DeGirolamo L, Baxter L, Smith K, Nakaji P, McGee S, Clark-Swanson K, Bendok B, Wu T, Hu L, Li J, Swanson K. NIMG-99. P53 AMPLIFICATION MODIFIES THE GLIOBLASTOMA MICROENVIRONMENT: DIFFERENTIATING THE CONTRIBUTION OF CELLS VS EDEMA IN THE T2 WEIGHTED MRI SIGNAL Neuro-Oncology. 19: vi164-vi164. DOI: 10.1093/Neuonc/Nox168.668  0.332
2017 Swanson KR, Gaw N, Hawkins-Daarud A, Jackson PR, Singleton KW, DeGirolamo L, Eschbacher J, Baxter L, Smith K, Nakaji P, McGee S, Clark-Swanson K, Bendok B, Dueck A, Wu T, et al. NIMG-74. RADIOMICS OF TUMOR INVASION 2.0: COMBINING MECHANISTIC TUMOR INVASION MODELS WITH MACHINE LEARNING MODELS TO ACCURATELY PREDICT TUMOR INVASION IN HUMAN GLIOBLASTOMA PATIENTS Neuro-Oncology. 19: vi159-vi159. DOI: 10.1093/Neuonc/Nox168.646  0.467
2016 Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-Oncology. PMID 27502248 DOI: 10.1093/Neuonc/Now135  0.61
2016 Chong CD, Gaw N, Fu Y, Li J, Wu T, Schwedt TJ. Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia : An International Journal of Headache. PMID 27306407 DOI: 10.1177/0333102416652091  0.387
2015 Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, Nakaji P, Plasencia J, Ranjbar S, Price SJ, Tran N, Loftus J, Jenkins R, O'Neill BP, Elmquist W, et al. Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma. Plos One. 10: e0141506. PMID 26599106 DOI: 10.1371/Journal.Pone.0141506  0.632
2015 Schwedt TJ, Chong CD, Wu T, Gaw N, Fu Y, Li J. Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging. Headache. 55: 762-77. PMID 26084235 DOI: 10.1111/Head.12584  0.352
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