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Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines., , , and . VLSI Signal Processing, 35 (3): 287-295 (2003)Ensembl 2018., , , , , , , , , and 47 other author(s). Nucleic Acids Research, 46 (Database-Issue): D754-D761 (2018)Ensembl 2019., , , , , , , , , and 58 other author(s). Nucleic Acids Research, 47 (Database-Issue): D745-D751 (2019)On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways., , , , and . EvoBIO, volume 4973 of Lecture Notes in Computer Science, page 200-211. Springer, (2008)Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction., , and . Analysis of Biological Data: A Soft Computing Approach, volume 3 of Science, Engineering, and Biology Informatics, World Scientific, (2007)Ab initio and homology based prediction of protein domains by recursive neural networks., , , , , and . BMC Bioinformatics, (2009)DISULFIND: a disulfide bonding state and cysteine connectivity prediction server., , , and . Nucleic Acids Research, 34 (Web-Server-Issue): 177-181 (2006)An adaptive model for learning molecular endpoints., , and . Similarity-based learning on structures, volume 09081 of Dagstuhl Seminar Proceedings, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany, (2009)A Recursive Connectionist Approach for Predicting Disulfide Connectivity in Proteins., and . SAC, page 67-71. ACM, (2003)Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment., , and . LION, volume 5851 of Lecture Notes in Computer Science, page 235-248. Springer, (2009)