| Year |
Citation |
Score |
| 2017 |
Fox DM, Tseng HA, Smolinski TG, Rotstein HG, Nadim F. Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents. Plos Computational Biology. 13: e1005565. PMID 28582395 DOI: 10.1371/journal.pcbi.1005565 |
0.52 |
|
| 2015 |
Ayyappan V, Kalavacharla V, Thimmapuram J, Bhide KP, Sripathi VR, Smolinski TG, Manoharan M, Thurston Y, Todd A, Kingham B, Mantovani R. Genome-wide profiling of histone modifications (H3K9me2 and H4K12ac) and gene expression in rust (uromyces appendiculatus) inoculated common bean (Phaseolus vulgaris L) Plos One. 10. DOI: 10.1371/journal.pone.0132176 |
1 |
|
| 2015 |
Harrington MA, Smolinski TG, Lloyd A, Shahin M. Undergraduate research programs can also be faculty development programs Diversity in Higher Education. 17: 115-127. DOI: 10.1108/S1479-364420150000017006 |
1 |
|
| 2013 |
Smolinski TG, Newell T, McDaniel S, Pokrajac D. Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets Proceedings of Spie - the International Society For Optical Engineering. 8857. DOI: 10.1117/12.2024580 |
1 |
|
| 2013 |
Smolinski TG, Prinz AA. Rough Sets and Neuroscience Intelligent Systems Reference Library. 43: 493-514. DOI: 10.1007/978-3-642-30341-8_26 |
1 |
|
| 2011 |
Prinz AA, Smolinski TG, Hudson AE. Understanding Animal-to-Animal Variability in Neuronal and Network Properties The Dynamic Brain: An Exploration of Neuronal Variability and Its Functional Significance. DOI: 10.1093/acprof:oso/9780195393798.003.0007 |
1 |
|
| 2010 |
Smolinski TG. Computer literacy for life sciences: helping the digital-era biology undergraduates face today's research. Cbe Life Sciences Education. 9: 357-63. PMID 20810969 DOI: 10.1187/cbe.10-03-0050 |
1 |
|
| 2010 |
Smolinski TG, Prinz AA. Rough sets for solving classification problems in computational neuroscience Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6086: 620-629. DOI: 10.1007/978-3-642-13529-3_66 |
1 |
|
| 2009 |
Smolinski TG, Prinz AA. Computational Intelligence in modeling of biological neurons: A case study of an invertebrate pacemaker neuron Proceedings of the International Joint Conference On Neural Networks. 2964-2970. DOI: 10.1109/IJCNN.2009.5178722 |
1 |
|
| 2008 |
Günay C, Smolinski TG, Lytton WW, Morse TM, Gleeson P, Crook S, Steuber V, Silver A, Voicu H, Andrews P, Bokil H, Maniar H, Loader C, Mehta S, Kleinfeld D, et al. Computational intelligence in electrophysiology: Trends and open problems Studies in Computational Intelligence. 122: 325-359. DOI: 10.1007/978-3-540-78534-7_14 |
1 |
|
| 2008 |
Hassanien AE, Milanova MG, Smolinski TG, Abraham A. Computational intelligence in solving bioinformatics problems: Reviews, perspectives, and challenges Studies in Computational Intelligence. 151: 3-47. DOI: 10.1007/978-3-540-70778-3_1 |
1 |
|
| 2007 |
Smolinski TG, Prinz AA, Zurada JM. Hybridization of rough sets and multi-objective evolutionary algorithms for classificatory signal decomposition Rough Computing: Theories, Technologies and Applications. 204-227. DOI: 10.4018/978-1-59904-552-8.ch010 |
1 |
|
| 2006 |
Smolinski TG, Buchanan R, Boratyn GM, Milanova M, Prinz AA. Independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials. Bmc Bioinformatics. 7: S8. PMID 17118151 DOI: 10.1186/1471-2105-7-S2-S8 |
1 |
|
| 2006 |
Smolinski TG, Milanova M, Boratyn GM, Buchanan R, Prinz AA. Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials 2006 Ieee International Conference On Granular Computing. 635-638. |
1 |
|
| 2006 |
Smolinski TG, Boratyn GM, Milanova M, Buchanan R, Prinz AA. Hybridization of independent component analysis, rough sets, and multi-objective evolutionary algorithms for classificatory decomposition of cortical evoked potentials Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4146: 174-183. |
1 |
|
| 2004 |
Boratyn GM, Smolinski TG, Zurada JM, Milanova M, Bhattacharyya S, Suva LJ. Hybridization of blind source separation and rough sets for proteomic biomarker identification Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 3070: 486-491. |
1 |
|
| 2004 |
Smolinski TG, Chenoweth DL, Zurada JM. Application of rough sets and neural networks to forecasting university facility and administrative cost recovery Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 3070: 538-543. |
1 |
|
| 2003 |
Smolinski TG, Chenoweth DL, Zurada JM. Time series prediction using rough sets and neural networks hybrid approach Proceedings of the Iasted International Conference On Neural Networks and Computational Intelligence. 108-111. |
1 |
|
| 2003 |
Boratyn GM, Smolinski TG, Milanova M, Zurada JM, Bhattacharyya S, Suva LJ. Scoring-Based Analysis of Protein Patterns for Identification of Myeloma Cancer Proceedings of the International Conference On Mathematics and Engineering Techniques in Medicine and Biological Sciences. 60-65. |
1 |
|
| 2003 |
Boratyn GM, Smolinski TG, Milanova M, Zurada JM, Bhattacharyya S, Suva LJ. Bayesian approach to analysis of protein patterns for identification of myeloma cancer International Conference On Machine Learning and Cybernetics. 2: 1217-1221. |
1 |
|
| 2002 |
Milanova M, Smolinski TG, Boratyn GM, Zurada JM, Wrobel A. Sparse correlation kernel analysis and evolutionary algorithm-based modeling of the sensory activity within the rat’s barrel cortex Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2388: 198-212. DOI: 10.1007/3-540-45665-1_16 |
1 |
|
| 2002 |
Smolinski TG, Boratyn GM, Milanova M, Zurada JM, Wrobel A. Evolutionary algorithms and rough sets-based hybrid approach to classificatory decomposition of cortical evoked potentials Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2475: 621-628. |
1 |
|
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