| A deep learning network approach to ab initio protein secondary structure prediction M Spencer, J Eickholt, J Cheng IEEE/ACM transactions on computational biology and bioinformatics (TCBB) 12 …, 2015 | 376 | 2015 |
| NNcon: improved protein contact map prediction using 2D-recursive neural networks AN Tegge, Z Wang, J Eickholt, J Cheng Nucleic acids research 37 (suppl 2), W515-W518, 2009 | 199 | 2009 |
| Predicting protein residue–residue contacts using deep networks and boosting J Eickholt, J Cheng Bioinformatics 28 (23), 3066-3072, 2012 | 191 | 2012 |
| Machine learning the voltage of electrode materials in metal-ion batteries RP Joshi, J Eickholt, L Li, M Fornari, V Barone, JE Peralta ACS applied materials & interfaces 11 (20), 18494-18503, 2019 | 144 | 2019 |
| Improving Protein Fold Recognition by Deep Learning Networks. T Jo, J Hou, J Eickholt, J Cheng Scientific reports 5, 17573-17573, 2014 | 141 | 2014 |
| A comprehensive overview of computational protein disorder prediction methods X Deng, J Eickholt, J Cheng Molecular BioSystems 8 (1), 114-121, 2012 | 123 | 2012 |
| DNdisorder: predicting protein disorder using boosting and deep networks J Eickholt, J Cheng BMC Bioinformatics 14 (1), 88, 2013 | 114 | 2013 |
| MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8 Z Wang, J Eickholt, J Cheng Bioinformatics 26 (7), 882-888, 2010 | 114 | 2010 |
| PreDisorder: ab initio sequence-based prediction of protein disordered regions X Deng, J Eickholt, J Cheng BMC bioinformatics 10 (1), 436, 2009 | 109 | 2009 |
| APOLLO: a quality assessment service for single and multiple protein models Z Wang, J Eickholt, J Cheng Bioinformatics 27 (12), 1715-1716, 2011 | 106 | 2011 |
| DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning J Eickholt, X Deng, J Cheng BMC bioinformatics 12 (1), 43, 2011 | 78 | 2011 |
| Machine Learning Screening of Metal-Ion Battery Electrode Materials IA Moses, RP Joshi, B Ozdemir, N Kumar, J Eickholt, V Barone ACS Applied Materials & Interfaces, 2021 | 73 | 2021 |
| Prediction of global and local quality of CASP8 models by MULTICOM series J Cheng, Z Wang, AN Tegge, J Eickholt Proteins: Structure, Function, and Bioinformatics 77 (S9), 181-184, 2009 | 73 | 2009 |
| The MULTICOM Toolbox for Protein Structure Prediction J Cheng, J Li, Z Wang, J Eickholt, X Deng BMC Bioinformatics 13 (1), 65, 2012 | 48 | 2012 |
| Characterizing the discussion of antibiotics in the twittersphere: what is the bigger picture? RL Kendra, S Karki, JL Eickholt, L Gandy Journal of medical Internet research 17 (6), e4220, 2015 | 46 | 2015 |
| Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11 T Liu, Y Wang, J Eickholt, Z Wang Scientific reports 6, 19301, 2016 | 39 | 2016 |
| A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks J Eickholt, J Cheng BMC bioinformatics 14 (Suppl 14), S12, 2013 | 39 | 2013 |
| Practical Active Learning Stations to Transform Existing Learning Environments Into Flexible, Active Learning Classrooms J Eickholt, MR Johnson, P Seeling IEEE Transactions on Education, 2020 | 36 | 2020 |
| Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish N Bravata, D Kelly, J Eickholt, J Bryan, S Miehls, D Zielinski Ecology and Evolution 10 (17), 9313-9325, 2020 | 30 | 2020 |
| Supporting Project-Based Learning through Economical and Flexible Learning Spaces J Eickholt, V Jogiparthi, P Seeling, Q Hinton, M Johnson Education Sciences 9 (3), 212, 2019 | 30 | 2019 |
