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Application of three graph Laplacian based semisupervised learning methods to protein function prediction problem

. International Journal on Bioinformatics & Biosciences (IJBB), Volume 3 (Number 2): 11-26 (June 2013)
DOI: 10.5121/ijbb.2013.3202

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Application of three graph Laplacian based semisupervised learning methods to protein function prediction problem. International Journal on Bioinformatics & Biosciences (IJBB), Volume 3 (Number 2): 11-26 (June 2013)High-dimensional MRI data analysis using a large-scale manifold learning approach., , , , , , and . Mach. Vis. Appl., 24 (5): 995-1014 (2013)Large-Scale Manifold Learning Using an Adaptive Sparse Neighbor Selection Approach for Brain Tumor Progression Prediction., , , and . MLMI, volume 8184 of Lecture Notes in Computer Science, page 219-226. Springer, (2013)Improving multi-atlas cardiac structure segmentation of computed tomography angiography: A performance evaluation based on a heterogeneous dataset., , , , , , and . Comput. Biol. Medicine, (2020)Adaptive graph construction for Isomap manifold learning., , , and . Image Processing: Algorithms and Systems, volume 9399 of SPIE Proceedings, page 939904. SPIE, (2015)Prediction of brain tumor progression using multiple histogram matched MRI scans., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 7963 of SPIE Proceedings, page 79632U. SPIE, (2011)High Dimensional Data Set Analysis Using a Large-Scale Manifold Learning Approach.. Old Dominion University, Norfolk, Virginia, USA, (2014)base-search.net (ftolddominionuni:oai:digitalcommons.odu.edu:ece_etds-1190).The Un-normalized Graph p-Laplacian Based Semi-supervised Learning Method and Protein Function Prediction Problem.. KSE (1), volume 244 of Advances in Intelligent Systems and Computing, page 23-35. Springer, (2013)A comparative study of two prediction models for brain tumor progression., , , and . Image Processing: Algorithms and Systems, volume 9399 of SPIE Proceedings, page 93990W. SPIE, (2015)Integrating Quaternion Graph Convolutional Networks with Tucker Decomposition for Link Prediction on Knowledge Graphs., , , and . KSEM (1), volume 13368 of Lecture Notes in Computer Science, page 614-626. Springer, (2022)