Supervised, semi-supervised and unsupervised inference of gene regulatory networks SR Maetschke, PB Madhamshettiwar, MJ Davis, MA Ragan Briefings in bioinformatics 15 (2), 195-211, 2014 | 198 | 2014 |
Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets PB Madhamshettiwar, SR Maetschke, MJ Davis, A Reverter, MA Ragan Genome medicine 4, 1-16, 2012 | 193 | 2012 |
A feature agnostic approach for glaucoma detection in OCT volumes S Maetschke, B Antony, H Ishikawa, G Wollstein, J Schuman, R Garnavi PloS one 14 (7), e0219126, 2019 | 192 | 2019 |
Gene Ontology-driven inference of protein–protein interactions using inducers SR Maetschke, M Simonsen, MJ Davis, MA Ragan Bioinformatics 28 (1), 69-75, 2012 | 95 | 2012 |
Semi-supervised segmentation of optic cup in retinal fundus images using variational autoencoder S Sedai, D Mahapatra, S Hewavitharanage, S Maetschke, R Garnavi Medical Image Computing and Computer-Assisted Intervention− MICCAI 2017 …, 2017 | 85 | 2017 |
Alignment-free inference of hierarchical and reticulate phylogenomic relationships G Bernard, CX Chan, Y Chan, XY Chua, Y Cong, JM Hogan, ... Briefings in Bioinformatics 20 (2), 426-435, 2019 | 83 | 2019 |
Exploiting structural and topological information to improve prediction of RNA-protein binding sites SR Maetschke, Z Yuan BMC bioinformatics 10, 1-14, 2009 | 67 | 2009 |
A novel hybrid approach for severity assessment of diabetic retinopathy in colour fundus images P Roy, R Tennakoon, K Cao, S Sedai, D Mahapatra, S Maetschke, ... 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 56 | 2017 |
Using satellite and aerial imagery for identification of solar PV: State of the art and research opportunities J de Hoog, S Maetschke, P Ilfrich, RR Kolluri Proceedings of the Eleventh ACM International Conference on Future Energy …, 2020 | 40 | 2020 |
BLOMAP: an encoding of amino acids which improves signal peptide cleavage site prediction S Maetschke, M Towsey, M Boden Proceedings of the 3rd Asia-Pacific bioinformatics conference, 141-150, 2005 | 37 | 2005 |
Characterizing cancer subtypes as attractors of Hopfield networks SR Maetschke, MA Ragan Bioinformatics 30 (9), 1273-1279, 2014 | 34 | 2014 |
Identifying novel peroxisomal proteins J Hawkins, D Mahony, S Maetschke, M Wakabayashi, RD Teasdale, ... Proteins: Structure, Function, and Bioinformatics 69 (3), 606-616, 2007 | 33 | 2007 |
Estimating global visual field indices in glaucoma by combining macula and optic disc OCT scans using 3-dimensional convolutional neural networks HH Yu, SR Maetschke, BJ Antony, H Ishikawa, G Wollstein, JS Schuman, ... Ophthalmology Glaucoma 4 (1), 102-112, 2021 | 32 | 2021 |
Genome-wide analysis of chlamydiae for promoters that phylogenetically footprint B Grech, S Maetschke, S Mathews, P Timms Research in microbiology 158 (8-9), 685-693, 2007 | 28 | 2007 |
mCOPA: analysis of heterogeneous features in cancer expression data C Wang, A Taciroglu, SR Maetschke, CC Nelson, MA Ragan, MJ Davis Journal of clinical bioinformatics 2, 1-11, 2012 | 27 | 2012 |
A visual framework for sequence analysis using n-grams and spectral rearrangement SR Maetschke, KS Kassahn, JA Dunn, SP Han, EZ Curley, KJ Stacey, ... Bioinformatics 26 (6), 737-744, 2010 | 23 | 2010 |
Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data S Roy, I Kiral, M Mirmomeni, T Mummert, A Braz, J Tsay, J Tang, U Asif, ... EBioMedicine 66, 2021 | 18 | 2021 |
Automatic selection of reference taxa for protein–protein interaction prediction with phylogenetic profiling M Simonsen, SR Maetschke, MA Ragan Bioinformatics 28 (6), 851-857, 2012 | 14 | 2012 |
Inference of visual field test performance from OCT volumes using deep learning S Maetschke, B Antony, H Ishikawa, G Wollstein, J Schuman, R Garnavi arXiv preprint arXiv:1908.01428, 2019 | 13 | 2019 |
Analysis of weld image to determine weld quality SR Maetschke US Patent 6,414,261, 2002 | 13 | 2002 |