{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T04:11:11Z","timestamp":1750824671217,"version":"3.41.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T00:00:00Z","timestamp":1501027200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T00:00:00Z","timestamp":1501027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1321053","IIS-1550705"],"award-info":[{"award-number":["IIS-1321053","IIS-1550705"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2018,1]]},"DOI":"10.1007\/s10618-017-0531-0","type":"journal-article","created":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T17:11:12Z","timestamp":1501089072000},"page":"218-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The inner and outer approaches to the design of recursive neural architectures"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8752-4664","authenticated-orcid":false,"given":"Pierre","family":"Baldi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,7,26]]},"reference":[{"key":"531_CR1","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1093\/bioinformatics\/15.11.937","volume":"15","author":"P Baldi","year":"1999","unstructured":"Baldi P, Brunak S, Frasconi P, Pollastri G, Soda G (1999) Exploiting the past and the future in protein secondary structure prediction. Bioinformatics 15:937\u2013946","journal-title":"Bioinformatics"},{"issue":"7","key":"531_CR2","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1162\/neco.1996.8.7.1541","volume":"8","author":"P Baldi","year":"1996","unstructured":"Baldi P, Chauvin Y (1996) Hybrid modeling, HMM\/NN architectures, and protein applications. Neural Comput 8(7):1541\u20131565","journal-title":"Neural Comput"},{"key":"531_CR3","first-page":"575","volume":"4","author":"P Baldi","year":"2003","unstructured":"Baldi P, Pollastri G (2003) The principled design of large-scale recursive neural network architectures\u2013DAG-RNNs and the protein structure prediction problem. J Mach Learn Res 4:575\u2013602","journal-title":"J Mach Learn Res"},{"issue":"3","key":"531_CR4","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1021\/ci034243x","volume":"44","author":"JS Delaney","year":"2004","unstructured":"Delaney JS (2004) ESOL: estimating aqueous solubility directly from molecular structure. J Chem Inf Comput Sci 44(3):1000\u20131005","journal-title":"J Chem Inf Comput Sci"},{"key":"531_CR5","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1093\/bioinformatics\/bts475","volume":"28","author":"P Di Lena","year":"2012","unstructured":"Di Lena P, Nagata K, Baldi P (2012) Deep architectures for protein contact map prediction. Bioinformatics 28:2449\u20132457. doi: 10.1093\/bioinformatics\/bts475","journal-title":"Bioinformatics"},{"key":"531_CR6","unstructured":"Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Advances in neural information processing systems, pp 2215\u20132223"},{"issue":"5","key":"531_CR7","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1109\/72.712151","volume":"9","author":"P Frasconi","year":"1998","unstructured":"Frasconi P, Gori M, Sperduti A (1998) A general framework for adaptive processing of data structures. IEEE Trans Neural Netw 9(5):768\u2013786","journal-title":"IEEE Trans Neural Netw"},{"issue":"10","key":"531_CR8","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451\u20132471","journal-title":"Neural Comput"},{"issue":"3","key":"531_CR9","first-page":"199","volume":"9","author":"RC Glen","year":"2006","unstructured":"Glen RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J (2006) Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 9(3):199","journal-title":"IDrugs"},{"key":"531_CR10","first-page":"347","volume":"1","author":"C Goller","year":"1996","unstructured":"Goller C, Kuchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. IEEE Int Conf Neural Netw 1:347\u2013352","journal-title":"IEEE Int Conf Neural Netw"},{"key":"531_CR11","unstructured":"Greff K, Srivastava RK, Koutnik J, Steunebrink BR, SchmidhuberJ (2015) LSTM: a search space odyssey. arXiv:1503.04069"},{"key":"531_CR12","doi-asserted-by":"publisher","unstructured":"Guest D, Collado J, Baldi P, Hsu S-C, Urban G, Whiteson D (2016) Jet flavor classification in high-energy physics with deep neural networks. Phys Rev D 94:112002. doi: 10.1103\/PhysRevD.94.112002","DOI":"10.1103\/PhysRevD.94.112002"},{"key":"531_CR13","volume-title":"Probabilistic Graphical Models: Principles and Techniques","author":"D Koller","year":"2009","unstructured":"Koller D, Friedman N (2009) Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Cambridge"},{"issue":"7","key":"531_CR14","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1021\/ci400187y","volume":"53","author":"A Lusci","year":"2013","unstructured":"Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 53(7):1563\u20131575","journal-title":"J Chem Inf Model"},{"issue":"18","key":"531_CR15","doi-asserted-by":"publisher","first-page":"2592","DOI":"10.1093\/bioinformatics\/btu352","volume":"30","author":"CN Magnan","year":"2014","unstructured":"Magnan CN, Baldi P (2014) SSpro\/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning, and structural similarity. Bioinformatics 30(18):2592\u20132597","journal-title":"Bioinformatics"},{"issue":"1","key":"531_CR16","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1002\/prot.22429","volume":"77","author":"C Mooney","year":"2009","unstructured":"Mooney C, Pollastri G (2009) Beyond the twilight zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information. Proteins Struct Funct Bioinform 77(1):181\u2013190","journal-title":"Proteins Struct Funct Bioinform"},{"key":"531_CR17","doi-asserted-by":"publisher","first-page":"032032","DOI":"10.1088\/1742-6596\/119\/3\/032032","volume":"119","author":"G Piacquadio","year":"2008","unstructured":"Piacquadio G, Weiser C (2008) A new inclusive secondary vertex algorithm for b-jet tagging in atlas. J Phys Conf Ser 119:032032","journal-title":"J Phys Conf Ser"},{"key":"531_CR18","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/0022-2836(88)90564-5","volume":"202","author":"N Qian","year":"1988","unstructured":"Qian N, Sejnowski TJ (1988) Predicting the secondary structure of glubular proteins using neural network models. J Mol Biol 202:865\u2013884","journal-title":"J Mol Biol"},{"key":"531_CR19","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1006\/jmbi.1993.1413","volume":"232","author":"B Rost","year":"1997","unstructured":"Rost B, Sander C (1997) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232:584\u2013599","journal-title":"J Mol Biol"},{"key":"531_CR20","doi-asserted-by":"crossref","unstructured":"Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), vol 1631, p 1642. Citeseer","DOI":"10.18653\/v1\/D13-1170"},{"issue":"suppl 2","key":"531_CR21","doi-asserted-by":"publisher","first-page":"W515","DOI":"10.1093\/nar\/gkp305","volume":"37","author":"AN Tegge","year":"2009","unstructured":"Tegge AN, Wang Z, Eickholt J, Cheng J (2009) NNcon: improved protein contact map prediction using 2D-recursive neural networks. Nucleic Acids Res 37(suppl 2):W515\u2013W518","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"531_CR22","doi-asserted-by":"publisher","first-page":"1392","DOI":"10.1016\/j.neunet.2008.02.002","volume":"21","author":"L Wu","year":"2008","unstructured":"Wu L, Baldi P (2008) Learning to play Go using recursive neural networks. Neural Netw 21(9):1392\u20131400","journal-title":"Neural Netw"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-017-0531-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-017-0531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-017-0531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T18:12:16Z","timestamp":1750788736000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-017-0531-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,26]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["531"],"URL":"https:\/\/doi.org\/10.1007\/s10618-017-0531-0","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2017,7,26]]},"assertion":[{"value":"31 May 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}