{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:46:05Z","timestamp":1761896765618},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319916408"},{"type":"electronic","value":"9783319916415"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-91641-5_13","type":"book-chapter","created":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T19:14:58Z","timestamp":1526066098000},"page":"151-162","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Construction of Heuristic for Protein Structure Optimization Using Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Rok","family":"Hribar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jurij","family":"\u0160ilc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregor","family":"Papa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,5,12]]},"reference":[{"issue":"Suppl. 2","key":"13_CR1","doi-asserted-by":"publisher","first-page":"W72","DOI":"10.1093\/nar\/gki396","volume":"33","author":"J Cheng","year":"2005","unstructured":"Cheng, J., Randall, A.Z., Sweredoski, M.J., Baldi, P.: SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res. 33(Suppl. 2), W72\u2013W76 (2005)","journal-title":"Nucleic Acids Res."},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.asoc.2016.04.001","volume":"45","author":"B Bo\u0161kovi\u0107","year":"2016","unstructured":"Bo\u0161kovi\u0107, B., Brest, J.: Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobic-polar model and cubic lattice. Appl. Soft Comput. 45, 61\u201370 (2016)","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1103\/PhysRevE.48.1469","volume":"48","author":"FH Stillinger","year":"1993","unstructured":"Stillinger, F.H., Head-Gordon, T., Hirshfeld, C.L.: Toy model for protein folding. Phys. Rev. E 48(2), 1469 (1993)","journal-title":"Phys. Rev. E"},{"issue":"14","key":"13_CR4","doi-asserted-by":"publisher","first-page":"6756","DOI":"10.1063\/1.1665529","volume":"120","author":"F Liang","year":"2004","unstructured":"Liang, F.: Annealing contour Monte Carlo algorithm for structure optimization in an off-lattice protein model. J. Chem. Phys. 120(14), 6756\u20136763 (2004)","journal-title":"J. Chem. Phys."},{"issue":"1","key":"13_CR5","doi-asserted-by":"publisher","first-page":"011916","DOI":"10.1103\/PhysRevE.72.011916","volume":"72","author":"SY Kim","year":"2005","unstructured":"Kim, S.Y., Lee, S.B., Lee, J.: Structure optimization by conformational space annealing in an off-lattice protein model. Phys. Rev. E 72(1), 011916 (2005)","journal-title":"Phys. Rev. E"},{"issue":"10","key":"13_CR6","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s00894-015-2806-y","volume":"21","author":"B Li","year":"2015","unstructured":"Li, B., Lin, M., Liu, Q., Li, Y., Zhou, C.: Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm. J. Mol. Model. 21(10), 261 (2015)","journal-title":"J. Mol. Model."},{"issue":"10","key":"13_CR7","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/s00894-016-3104-z","volume":"22","author":"B Bo\u0161kovi\u0107","year":"2016","unstructured":"Bo\u0161kovi\u0107, B., Brest, J.: Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model. J. Mol. Model. 22(10), 252 (2016)","journal-title":"J. Mol. Model."},{"issue":"2","key":"13_CR8","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1002\/prot.10082","volume":"47","author":"G Pollastri","year":"2002","unstructured":"Pollastri, G., Przybylski, D., Rost, B., Baldi, P.: Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins: Struct. Funct. Bioinf. 47(2), 228\u2013235 (2002)","journal-title":"Proteins: Struct. Funct. Bioinf."},{"issue":"3","key":"13_CR9","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s10618-005-0001-y","volume":"11","author":"J Cheng","year":"2005","unstructured":"Cheng, J., Sweredoski, M.J., Baldi, P.: Accurate prediction of protein disordered regions by mining protein structure data. Data Min. Knowl. Disc. 11(3), 213\u2013222 (2005)","journal-title":"Data Min. Knowl. Disc."},{"issue":"19","key":"13_CR10","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1093\/bioinformatics\/bts475","volume":"28","author":"P Lena Di","year":"2012","unstructured":"Di Lena, P., Nagata, K., Baldi, P.: Deep architectures for protein contact map prediction. Bioinformatics 28(19), 2449\u20132457 (2012)","journal-title":"Bioinformatics"},{"issue":"1","key":"13_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejor.2013.02.053","volume":"231","author":"T Vidal","year":"2013","unstructured":"Vidal, T., Crainic, T.G., Gendreau, M., Prins, C.: Heuristics for multi-attribute vehicle routing problems: a survey and synthesis. Eur. J. Oper. Res. 231(1), 1\u201321 (2013)","journal-title":"Eur. J. Oper. Res."},{"issue":"1","key":"13_CR12","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/TEVC.2015.2429314","volume":"20","author":"J Branke","year":"2016","unstructured":"Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110\u2013124 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"13_CR13","doi-asserted-by":"publisher","first-page":"210a","DOI":"10.1016\/j.bpj.2014.11.1164","volume":"108","author":"A Perez","year":"2015","unstructured":"Perez, A., MacCallum, J., Dill, K.A.: Using physics and heuristics in protein structure prediction. Biophys. J. 108(2), 210a (2015)","journal-title":"Biophys. J ."},{"issue":"2","key":"13_CR14","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251\u2013257 (1991)","journal-title":"Neural Netw."},{"key":"13_CR15","unstructured":"Dauphin, Y.N., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., Bengio, Y.: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In: Advances in Neural Information Processing Systems, pp. 2933\u20132941 (2014)"},{"key":"13_CR16","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). \nhttp:\/\/www.deeplearningbook.org"},{"key":"13_CR17","unstructured":"Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., Riedmiller, M.: Deterministic policy gradient algorithms. In: ICML (2014)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Ling, A.C., Aydonat, U., O\u2019Connell, S., Capalija, D., Chiu, G.R.: Creating high performance applications with Intel\u2019s FPGA OpenCL SDK. In: Proceedings of the 5th International Workshop on OpenCL. ACM (2017). Article No. 11","DOI":"10.1145\/3078155.3078169"},{"issue":"3","key":"13_CR19","doi-asserted-by":"publisher","first-page":"031906","DOI":"10.1103\/PhysRevE.71.031906","volume":"71","author":"M Bachmann","year":"2005","unstructured":"Bachmann, M., Ark\u0131n, H., Janke, W.: Multicanonical study of coarse-grained off-lattice models for folding heteropolymers. Phys. Rev. E 71(3), 031906 (2005)","journal-title":"Phys. Rev. E"},{"issue":"19","key":"13_CR20","doi-asserted-by":"publisher","first-page":"5425","DOI":"10.1021\/bi00367a013","volume":"25","author":"J Parker","year":"1986","unstructured":"Parker, J., Guo, D., Hodges, R.: New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25(19), 5425\u20135432 (1986)","journal-title":"Biochemistry"},{"issue":"24","key":"13_CR21","doi-asserted-by":"publisher","first-page":"7484","DOI":"10.1073\/pnas.1507565112","volume":"112","author":"R Wolfenden","year":"2015","unstructured":"Wolfenden, R., Lewis, C.A., Yuan, Y., Carter, C.W.: Temperature dependence of amino acid hydrophobicities. Proc. Nat. Acad. Sci. 112(24), 7484\u20137488 (2015)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"13_CR22","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint \narXiv:1412.6980\n\n (2014)"},{"key":"13_CR23","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"}],"container-title":["Lecture Notes in Computer Science","Bioinspired Optimization Methods and Their Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-91641-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T19:19:04Z","timestamp":1526066344000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-91641-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319916408","9783319916415"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-91641-5_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]}}}