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A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images.

, , , , , and . Front. Neuroinform., (2019)

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Prediction of dinucleotide-specific RNA-binding sites in proteins., , , , , and . BMC Bioinformatics, 12 (S-13): S5 (2011)Neural network-based prediction of transmembrane -strand segments in outer membrane proteins., , and . Journal of Computational Chemistry, 25 (5): 762-767 (2004)NETASA: neural network based prediction of solvent accessibility., and . Bioinformatics, 18 (6): 819-824 (2002)A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images., , , , , and . Front. Neuroinform., (2019)Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins., , , , , and . BMC Bioinformatics, (2010)Application of residue distribution along the sequence for discriminating outer membrane proteins., , and . Computational Biology and Chemistry, 29 (2): 135-142 (2005)Human Blood-Brain Differential Gene-Expression Correlates with Dipeptide Frequency of Gene Products.. ISBRA, volume 4983 of Lecture Notes in Computer Science, page 504-508. Springer, (2008)ATP-binding site as a further application of neural networks to residue level prediction., and . IJCNN, page 2430-2434. IEEE, (2008)TMBETA-NET: discrimination and prediction of membrane spanning ß-strands in outer membrane proteins., , and . Nucleic Acids Research, 33 (Web-Server-Issue): 164-167 (2005)Design and training of a neural network for predicting the solvent accessibility of proteins., and . Journal of Computational Chemistry, 24 (11): 1313-1320 (2003)