| Year |
Citation |
Score |
| 2024 |
Li Y, Song Y, Sui J, Greiner R, Li XM, Greenshaw AJ, Liu YS, Cao B. Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study. Journal of Affective Disorders. PMID 38670463 DOI: 10.1016/j.jad.2024.04.098 |
0.514 |
|
| 2023 |
Song Y, Liu YS, Talarico F, Zhang Y, Hayward J, Wang M, Stroulia E, Dixon RA, Greiner R, Li X, Greenshaw A, Jie S, Cao B. Associations between differential aging and lifestyle, environment, current, and future health conditions: Findings from Canadian Longitudinal Study on Aging. Gerontology. PMID 37725932 DOI: 10.1159/000534015 |
0.423 |
|
| 2023 |
Song Y, Qian L, Sui J, Greiner R, Li XM, Greenshaw AJ, Liu YS, Cao B. Corrigendum to "Prediction of depression onset risk among middle-aged and elderly adults using machine learning and Canadian Longitudinal Study on Aging cohort" [J. Affect. Disord. vol. 339 (2023) page 52-57]. Journal of Affective Disorders. PMID 37684107 DOI: 10.1016/j.jad.2023.08.124 |
0.5 |
|
| 2023 |
Song Y, Qian L, Sui J, Greiner R, Li XM, Greenshaw AJ, Liu YS, Cao B. Prediction of depression onset risk among middle-aged and elderly adults using machine learning and Canadian Longitudinal Study on Aging cohort. Journal of Affective Disorders. PMID 37380110 DOI: 10.1016/j.jad.2023.06.031 |
0.468 |
|
| 2022 |
Yousefnezhad M, Zhang D, Greenshaw AJ, Greiner R. Editorial: Multi-site neuroimage analysis: Domain adaptation and batch effects. Frontiers in Neuroinformatics. 16: 994463. PMID 36120084 DOI: 10.3389/fninf.2022.994463 |
0.699 |
|
| 2022 |
Paul AK, Bose A, Kalmady SV, Shivakumar V, Sreeraj VS, Parlikar R, Narayanaswamy JC, Dursun SM, Greenshaw AJ, Greiner R, Venkatasubramanian G. Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study. Frontiers in Psychiatry. 13: 923938. PMID 35990061 DOI: 10.3389/fpsyt.2022.923938 |
0.506 |
|
| 2022 |
Liu YS, Kiyang L, Hayward J, Zhang Y, Metes D, Wang M, Svenson LW, Talarico F, Chue P, Li XM, Greiner R, Greenshaw AJ, Cao B. Individualized Prospective Prediction of Opioid Use Disorder. Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie. 7067437221114094. PMID 35892186 DOI: 10.1177/07067437221114094 |
0.5 |
|
| 2022 |
Obuobi-Donkor G, Eboreime E, Bond J, Phung N, Eyben S, Hayward J, Zhang Y, MacMaster F, Clelland S, Greiner R, Jones C, Cao B, Brémault-Phillips S, Wells K, Li XM, et al. An E-Mental Health Solution to Prevent and Manage Posttraumatic Stress Injuries Among First Responders in Alberta: Protocol for the Implementation and Evaluation of Text Messaging Services (Text4PTSI and Text4Wellbeing). Jmir Research Protocols. 11: e30680. PMID 35468094 DOI: 10.2196/30680 |
0.432 |
|
| 2022 |
Sawalha J, Yousefnezhad M, Shah Z, Brown MRG, Greenshaw AJ, Greiner R. Detecting Presence of PTSD Using Sentiment Analysis From Text Data. Frontiers in Psychiatry. 12: 811392. PMID 35178000 DOI: 10.3389/fpsyt.2021.811392 |
0.743 |
|
| 2021 |
Kalmady SV, Paul AK, Narayanaswamy JC, Agrawal R, Shivakumar V, Greenshaw AJ, Dursun SM, Greiner R, Venkatasubramanian G, Reddy YCJ. Prediction of Obsessive-Compulsive Disorder: Importance of neurobiology-aided feature design and cross-diagnosis transfer learning. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging. PMID 34929344 DOI: 10.1016/j.bpsc.2021.12.003 |
0.54 |
|
| 2021 |
Cao B, Liu YS, Selvitella A, Librenza-Garcia D, Passos IC, Sawalha J, Ballester P, Chen J, Dong S, Wang F, Kapczinski F, Dursun SM, Li XM, Greiner R, Greenshaw A. Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study. Scientific Reports. 11: 21301. PMID 34716400 DOI: 10.1038/s41598-021-99534-z |
0.502 |
|
| 2021 |
Sawalha J, Yousefnezhad M, Selvitella AM, Cao B, Greenshaw AJ, Greiner R. Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces. Scientific Reports. 11: 16723. PMID 34408203 DOI: 10.1038/s41598-021-95987-4 |
0.734 |
|
| 2021 |
Benoit JRA, Dursun SM, Greiner R, Cao B, Brown MRG, Lam RW, Greenshaw AJ. Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine : Utiliser l'apprentissage machine pour prédire la rémission chez les patients souffrant de trouble dépressif majeur traités par desvenlafaxine. Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie. 7067437211037141. PMID 34379019 DOI: 10.1177/07067437211037141 |
0.529 |
|
| 2021 |
Shalaby R, Vuong W, Hrabok M, Gusnowski A, Mrklas K, Li D, Snaterse M, Surood S, Cao B, Li XM, Greiner R, Greenshaw AJ, Agyapong VIO. COVID-19 Pandemic: Gender difference in satisfaction with a daily supportive text message program (Text4Hope) and anticipated receptivity for technology-based health support during emergencies-Cross Sectional Survey. Jmir Mhealth and Uhealth. PMID 33750738 DOI: 10.2196/24184 |
0.455 |
|
| 2021 |
Agyapong VIO, Shalaby R, Hrabok M, Vuong W, Noble JM, Gusnowski A, Mrklas K, Li D, Snaterse M, Surood S, Cao B, Li XM, Greiner R, Greenshaw AJ. Mental Health Outreach via Supportive Text Messages during the COVID-19 Pandemic: Improved Mental Health and Reduced Suicidal Ideation after Six Weeks in Subscribers of Text4Hope Compared to a Control Population. International Journal of Environmental Research and Public Health. 18. PMID 33672120 DOI: 10.3390/ijerph18042157 |
0.472 |
|
| 2021 |
Agyapong VIO, Hrabok M, Shalaby R, Vuong W, Noble JM, Gusnowski A, Mrklas K, Li D, Urichuck L, Snaterse M, Surood S, Cao B, Li XM, Greiner R, Greenshaw AJ. Text4Hope: Receiving Daily Supportive Text Messages for Three Months during the COVID-19 Pandemic Reduces Stress, Anxiety, and Depression. Disaster Medicine and Public Health Preparedness. 1-15. PMID 33551009 DOI: 10.1017/dmp.2021.27 |
0.451 |
|
| 2020 |
Sawalha J, Cao L, Chen J, Selvitella A, Liu Y, Yang C, Li X, Zhang X, Sun J, Zhang Y, Zhao L, Cui L, Zhang Y, Sui J, Greiner R, et al. Individualized identification of first-episode bipolar disorder using machine learning and cognitive tests. Journal of Affective Disorders. 282: 662-668. PMID 33445089 DOI: 10.1016/j.jad.2020.12.046 |
0.483 |
|
| 2020 |
Agyapong VIO, Hrabok M, Vuong W, Shalaby R, Noble JM, Gusnowski A, Mrklas K, Li D, Urichuk L, Snaterse M, Surood S, Cao B, Li XM, Greiner R, Greenshaw AJ. Mental Health Response to the COVID-19 Pandemic: Effectiveness of a Daily Supportive Text Message (Text4Hope) Program at Six Weeks in Reducing Stress, Anxiety, and Depression in Subscribers. Jmir Mental Health. PMID 33296330 DOI: 10.2196/22423 |
0.454 |
|
| 2020 |
Kalmady SV, Paul AK, Greiner R, Agrawal R, Amaresha AC, Shivakumar V, Narayanaswamy JC, Greenshaw AJ, Dursun SM, Venkatasubramanian G. Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives. Npj Schizophrenia. 6: 30. PMID 33159092 DOI: 10.1038/s41537-020-00119-y |
0.495 |
|
| 2020 |
Agyapong VIO, Hrabok M, Vuong W, Gusnowski A, Shalaby R, Mrklas K, Li D, Urichuck L, Snaterse M, Surood S, Cao B, Li XM, Greiner R, Greenshaw AJ. COVID-19: Closing the Psychological Treatment Gap during the Pandemic, a Protocol for Implementation and Evaluation of Text4Hope (a Supportive Text Message Program). Jmir Research Protocols. PMID 32501805 DOI: 10.2196/19292 |
0.502 |
|
| 2020 |
Ghoreishiamiri R, Little G, Brown MRG, Greiner R. A simple classification framework for predicting Alzheimer’s disease from region-based grey matter volume and APOE genotype status Artificial Intelligence Research. 8: 15. DOI: 10.5430/Air.V8N2P15 |
0.334 |
|
| 2019 |
Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, Greenshaw AJ, Dursun SM, Venkatasubramanian G. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. Npj Schizophrenia. 5: 2. PMID 30659193 DOI: 10.1038/s41537-018-0070-8 |
0.505 |
|
| 2019 |
Stirnimann G, Ebady M, Hoehn B, Simone IBD, Mazurak VC, Greiner R, Tandon P, Montano-Loza AJ. SAT-121-Predicting sarcopenia in patients with cirrhosis based on clinical and laboratory parameters using machine learning Journal of Hepatology. 70. DOI: 10.1016/S0618-8278(19)31360-X |
0.338 |
|
| 2018 |
Xiao Y, Greiner R, Lewis MA. Correction to: Evaluation of machine learning methods for predicting eradication of aquatic invasive species Biological Invasions. 20: 2505-2506. DOI: 10.1007/S10530-018-1730-3 |
0.323 |
|
| 2017 |
Liang S, Vega R, Kong X, Deng W, Wang Q, Ma X, Li M, Hu X, Greenshaw AJ, Greiner R, Li T. Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features. Neuroscience Bulletin. PMID 29098645 DOI: 10.1007/S12264-017-0190-6 |
0.46 |
|
| 2017 |
Liang S, Brown MRG, Deng W, Wang Q, Ma X, Li M, Hu X, Juhas M, Li X, Greiner R, Greenshaw AJ, Li T. Convergence and divergence of neurocognitive patterns in schizophrenia and depression. Schizophrenia Research. PMID 28651909 DOI: 10.1016/J.Schres.2017.06.004 |
0.436 |
|
| 2017 |
Gheiratmand M, Rish I, Cecchi GA, Brown MRG, Greiner R, Polosecki PI, Bashivan P, Greenshaw AJ, Ramasubbu R, Dursun SM. Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms. Npj Schizophrenia. 3: 22. PMID 28560268 DOI: 10.1038/s41537-017-0022-8 |
0.485 |
|
| 2016 |
Ghiassian S, Greiner R, Jin P, Brown MR. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism. Plos One. 11: e0166934. PMID 28030565 DOI: 10.1371/Journal.Pone.0166934 |
0.329 |
|
| 2016 |
Ramasubbu R, Brown MR, Cortese F, Gaxiola I, Goodyear B, Greenshaw AJ, Dursun SM, Greiner R. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage. Clinical. 12: 320-31. PMID 27551669 DOI: 10.1016/J.Nicl.2016.07.012 |
0.57 |
|
| 2016 |
Vega R, Sajed T, Mathewson KW, Khare K, Pilarski PM, Greiner R, Sánchez-Ante G, Antelis JM. Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals Artificial Intelligence Review. 6: 37-51. DOI: 10.5430/Air.V6N1P37 |
0.336 |
|
| 2014 |
Allen F, Pon A, Wilson M, Greiner R, Wishart D. CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Research. 42: W94-9. PMID 24895432 DOI: 10.1093/Nar/Gku436 |
0.313 |
|
| 2013 |
Bastani M, Vos L, Asgarian N, Deschenes J, Graham K, Mackey J, Greiner R. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status. Plos One. 8: e82144. PMID 24312637 DOI: 10.1371/Journal.Pone.0082144 |
0.323 |
|
| 2013 |
Eisner R, Greiner R, Tso V, Wang H, Fedorak RN. A machine-learned predictor of colonic polyps based on urinary metabolomics. Biomed Research International. 2013: 303982-303982. PMID 24307992 DOI: 10.1155/2013/303982 |
0.331 |
|
| 2013 |
Hajiloo M, Sapkota Y, Mackey JR, Robson P, Greiner R, Damaraju S. ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction. Bmc Bioinformatics. 14: 61. PMID 23432980 DOI: 10.1186/1471-2105-14-61 |
0.316 |
|
| 2012 |
Sidhu GS, Asgarian N, Greiner R, Brown MR. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Frontiers in Systems Neuroscience. 6: 74. PMID 23162439 DOI: 10.3389/Fnsys.2012.00074 |
0.305 |
|
| 2012 |
Brown MR, Sidhu GS, Greiner R, Asgarian N, Bastani M, Silverstone PH, Greenshaw AJ, Dursun SM. ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Frontiers in Systems Neuroscience. 6: 69. PMID 23060754 DOI: 10.3389/Fnsys.2012.00069 |
0.529 |
|
| 2011 |
Vaisipour S, Greiner R, Wishart D, Bastani M, Yu C. Learning predictors by integrating multiple microarray datasets F1000research. 2. DOI: 10.7490/F1000Research.1294.1 |
0.34 |
|
| 2010 |
Kerhet A, Small C, Quon H, Riauka T, Schrader L, Greiner R, Yee D, McEwan A, Roa W. Application of machine learning methodology for PET-based definition of lung cancer. Current Oncology (Toronto, Ont.). 17: 41-7. PMID 20179802 DOI: 10.3747/Co.V17I1.394 |
0.314 |
|
| 2010 |
Asgarian N, Hu X, Aktary Z, Chapman KA, Lam L, Chibbar R, Mackey J, Greiner R, Pasdar M. Learning to predict relapse in invasive ductal carcinomas based on the subcellular localization of junctional proteins. Breast Cancer Research and Treatment. 121: 527-38. PMID 19787450 DOI: 10.1007/S10549-009-0557-0 |
0.335 |
|
| 2010 |
Schulte O, Luo W, Greiner R. Mind change optimal learning of Bayes net structure from dependency and independency data Information and Computation. 208: 63-82. DOI: 10.1016/J.Ic.2009.03.009 |
0.314 |
|
| 2009 |
Su X, Khoshgoftaar TM, Greiner R. Making an accurate classifier ensemble by voting on classifications from imputed learning sets International Journal of Information and Decision Sciences. 1: 301-322. DOI: 10.1504/Ijids.2009.027657 |
0.337 |
|
| 2007 |
Li L, Bulitko V, Greiner R. Focus of Attention in Reinforcement Learning Journal of Universal Computer Science. 13: 1246-1269. DOI: 10.7939/R31G0Hx9N |
0.323 |
|
| 2006 |
Morris M, Greiner R, Sander J, Murtha A, Schmidt MW. Learning a Classification-based Glioma Growth Model Using MRI Data Journal of Computers. 1: 21-31. DOI: 10.4304/Jcp.1.7.21-31 |
0.335 |
|
| 2006 |
Morris M, Greiner R, Sander J, Murtha A, Schmidt M. A classification-based glioma diffusion model using MRI data Lecture Notes in Computer Science. 98-109. DOI: 10.1007/11766247_9 |
0.313 |
|
| 2004 |
Szafron D, Lu P, Greiner R, Wishart DS, Poulin B, Eisner R, Lu Z, Anvik J, Macdonell C, Fyshe A, Meeuwis D. Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Research. 32: W365-71. PMID 15215412 DOI: 10.1093/Nar/Gkh485 |
0.313 |
|
| 2004 |
Lu Z, Szafron D, Greiner R, Lu P, Wishart DS, Poulin B, Anvik J, Macdonell C, Eisner R. Predicting subcellular localization of proteins using machine-learned classifiers Bioinformatics. 20: 547-556. PMID 14990451 DOI: 10.1093/Bioinformatics/Btg447 |
0.356 |
|
| 2002 |
Greiner R, Grove AJ, Roth D. Learning cost-sensitive active classifiers Artificial Intelligence. 139: 137-174. DOI: 10.1016/S0004-3702(02)00209-6 |
0.315 |
|
| 2002 |
Cheng J, Greiner R, Kelly J, Bell D, Liu W. Learning Bayesian networks from data: an information-theory based approach Artificial Intelligence. 137: 43-90. DOI: 10.1016/S0004-3702(02)00191-1 |
0.323 |
|
| 2001 |
Cheng J, Greiner R. Learning Bayesian Belief Network Classifiers: Algorithms and System Lecture Notes in Computer Science. 141-151. DOI: 10.1007/3-540-45153-6_14 |
0.356 |
|
| 1997 |
Greiner R, Grove AJ, Kogan A. Knowing what doesn't matter: exploiting the omission of irrelevant data Artificial Intelligence. 97: 345-380. DOI: 10.1016/S0004-3702(97)00048-9 |
0.304 |
|
| 1988 |
Greiner R, Silver B, Becker S, Grüninger M. A Review of Machine Learning at AAAI-87 Machine Learning. 3: 79-92. DOI: 10.1023/A:1022637632387 |
0.315 |
|
| 1988 |
Greiner R. Learning by understanding analogies Artificial Intelligence. 35: 81-125. DOI: 10.1016/0004-3702(88)90032-X |
0.314 |
|
| Show low-probability matches. |