Jesse Eickholt
Cited by
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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
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
Predicting protein residue–residue contacts using deep networks and boosting
J Eickholt, J Cheng
Bioinformatics 28 (23), 3066-3072, 2012
Improving Protein Fold Recognition by Deep Learning Networks.
T Jo, J Hou, J Eickholt, J Cheng
Scientific reports 5, 17573-17573, 2014
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
A comprehensive overview of computational protein disorder prediction methods
X Deng, J Eickholt, J Cheng
Molecular BioSystems 8 (1), 114-121, 2012
DNdisorder: predicting protein disorder using boosting and deep networks
J Eickholt, J Cheng
BMC Bioinformatics 14 (1), 88, 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
PreDisorder: ab initio sequence-based prediction of protein disordered regions
X Deng, J Eickholt, J Cheng
BMC bioinformatics 10 (1), 436, 2009
APOLLO: a quality assessment service for single and multiple protein models
Z Wang, J Eickholt, J Cheng
Bioinformatics 27 (12), 1715-1716, 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
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
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
The MULTICOM Toolbox for Protein Structure Prediction
J Cheng, J Li, Z Wang, J Eickholt, X Deng
BMC Bioinformatics 13 (1), 65, 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
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
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
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
Designing and benchmarking the MULTICOM protein structure prediction system
J Li, X Deng, J Eickholt, J Cheng
BMC structural biology 13 (1), 2, 2013
An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions
X Deng, J Gumm, S Karki, J Eickholt, J Cheng
International Journal of Molecular Sciences 16 (7), 15384-15404, 2015
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