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Evaluation of colorectal cancer subtypes and cell lines using deep learning

, , and . Life science alliance, (2019)

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Ranking models of transmembrane β-barrel proteins using Z-coordinate predictions., and . Bioinform., 28 (12): 90-96 (2012)Evaluation of colorectal cancer subtypes and cell lines using deep learning, , and . Life science alliance, (2019)Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins., , , , and . Bioinform., 32 (10): 1571-1573 (2016)MACA: marker-based automatic cell-type annotation for single-cell expression data., , , and . Bioinform., 38 (6): 1756-1760 (2022)Prediction of the burial status of transmembrane residues of helical membrane proteins., , and . BMC Bioinform., (2007)PconsFold: improved contact predictions improve protein models., , , , , and . Bioinform., 30 (17): 482-488 (2014)Categorised Counting Mediated by Blotting Membrane Systems for Particle-Based Data Mining and Numerical Algorithms., , , , and . Int. Conf. on Membrane Computing, volume 8961 of Lecture Notes in Computer Science, page 241-257. Springer, (2014)BOCTOPUS: improved topology prediction of transmembrane β barrel proteins., and . Bioinform., 28 (4): 516-522 (2012)Statistical analysis and exposure status classification of transmembrane beta barrel residues., , and . Comput. Biol. Chem., 35 (2): 96-107 (2011)Towards in vivo computing: quantitative analysis of an artificial gene regulatory network behaving as a RS flip-flop and simulating the system in silico., , , , and . BIONETICS, page 5. ACM, (2006)