{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:23:38Z","timestamp":1763468618726,"version":"3.40.3"},"publisher-location":"New York, NY","reference-count":29,"publisher":"Springer US","isbn-type":[{"type":"print","value":"9781071608258"},{"type":"electronic","value":"9781071608265"}],"license":[{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-1-0716-0826-5_15","type":"book-chapter","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T12:47:58Z","timestamp":1598878078000},"page":"307-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Hybrid Levenberg\u2013Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models"],"prefix":"10.1007","author":[{"given":"Eshel","family":"Faraggi","sequence":"first","affiliation":[]},{"given":"Robert L.","family":"Jernigan","sequence":"additional","affiliation":[]},{"given":"Andrzej","family":"Kloczkowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,18]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/RBME.2008.2008239","volume":"1","author":"J Cheng","year":"2008","unstructured":"Cheng J, Tegge AN, Baldi P (2008) Machine learning methods for protein structure prediction. IEEE Rev Biomed Eng 1:41\u201349","journal-title":"IEEE Rev Biomed Eng"},{"issue":"3","key":"15_CR2","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.compbiolchem.2009.04.004","volume":"33","author":"P Jain","year":"2009","unstructured":"Jain P, Garibaldi JM, Hirst JD (2009) Supervised machine learning algorithms for protein structure classification. Comput Biol Chem 33(3):216\u2013223","journal-title":"Comput Biol Chem"},{"issue":"5","key":"15_CR3","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1002\/prot.24454","volume":"82","author":"E Faraggi","year":"2014","unstructured":"Faraggi E, Kloczkowski, A (2014) A global machine learning based scoring function for protein structure prediction. Proteins Struct Funct Bioinf 82(5):752\u2013759","journal-title":"Proteins Struct Funct Bioinf"},{"issue":"3","key":"15_CR4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1517\/17460441.2016.1146250","volume":"11","author":"AN Lima","year":"2016","unstructured":"Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM (2016) Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov 11(3):225\u2013239","journal-title":"Expert Opin Drug Discov"},{"key":"15_CR5","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1146\/annurev-biodatasci-080917-013343","volume":"1","author":"P Baldi","year":"2018","unstructured":"Baldi P (2018). Deep learning in biomedical data science. Annu Rev Biomed Data Sci 1:181\u2013205","journal-title":"Annu Rev Biomed Data Sci"},{"issue":"4","key":"15_CR6","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1002\/prot.20264","volume":"57","author":"Y Zhang","year":"2004","unstructured":"Zhang Y, Skolnick J (2004) Scoring function for automated assessment of protein structure template quality. Proteins Struct Funct Bioinf 57(4):702\u2013710","journal-title":"Proteins Struct Funct Bioinf"},{"issue":"7","key":"15_CR7","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1093\/nar\/gki524","volume":"33","author":"Y Zhang","year":"2005","unstructured":"Zhang Y, Skolnick J (2005) Tm-align: a protein structure alignment algorithm based on the tm-score. Nucleic Acids Res 33(7):2302\u20132309","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"15_CR8","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1093\/bioinformatics\/btq066","volume":"26","author":"J Xu","year":"2010","unstructured":"Xu J, Zhang Y (2010) How significant is a protein structure similarity with tm-score= 0.5? Bioinformatics 26(7):889\u2013895","journal-title":"Bioinformatics"},{"issue":"11","key":"15_CR9","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1002\/prot.24682","volume":"82","author":"E Faraggi","year":"2014","unstructured":"Faraggi E, Zhou Y, Kloczkowski A (2014) Accurate single-sequence prediction of solvent accessible surface area using local and global features. Proteins Struct Funct Bioinf 82(11):3170\u20133176","journal-title":"Proteins Struct Funct Bioinf"},{"issue":"7","key":"15_CR10","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/S0969-2126(99)80097-5","volume":"7","author":"S Chakravarty","year":"1999","unstructured":"Chakravarty S, Varadarajan R (1999) Residue depth: a novel parameter for the analysis of protein structure and stability. Structure 7(7):723\u2013732","journal-title":"Structure"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Tan KP, Varadarajan R, Madhusudhan MS (2011) Depth: a web server to compute depth and predict small-molecule binding cavities in proteins. Nucleic Acids Res 39(suppl_2):W242\u2013W248","DOI":"10.1093\/nar\/gkr356"},{"issue":"1","key":"15_CR12","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","volume":"28","author":"HM Berman","year":"2000","unstructured":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235\u2013242","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"15_CR13","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1038\/nsb1203-980","volume":"10","author":"H Berman","year":"2003","unstructured":"Berman H, Henrick K, Nakamura H (2003) Announcing the worldwide protein data bank. Nat Struct Mol Biol 10(12):980","journal-title":"Nat Struct Mol Biol"},{"key":"15_CR14","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/978-1-4939-2239-0_10","volume-title":"Artificial neural networks","author":"E Faraggi","year":"2015","unstructured":"Faraggi E, Kloczkowski, A (2015) Genn: a general neural network for learning tabulated data with examples from protein structure prediction. In: Artificial neural networks. Springer, Berlin, pp 165\u2013178"},{"issue":"2","key":"15_CR15","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179\u2013211","journal-title":"Cogn Sci"},{"key":"15_CR16","first-page":"25","volume-title":"Proceedings of the 2000 conference on intelligent systems for molecular biology (ISMB00)","author":"P Baldi","year":"2000","unstructured":"Baldi P, Pollastri G, Andersen CAF, Brunak S (2000) Matching protein beta-sheet partners by feedforward and recurrent neural networks. In: Proceedings of the 2000 conference on intelligent systems for molecular biology (ISMB00), La Jolla. AAAI Press, Palo Alto, pp 25\u201336"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Pollastri G, Baldi P, Fariselli P, Casadio R (2001) Improved prediction of the number of residue contacts in proteins by recurrent neural networks. Bioinformatics 17(suppl_1):S234\u2013S242","DOI":"10.1093\/bioinformatics\/17.suppl_1.S234"},{"issue":"3","key":"15_CR18","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.eswa.2005.04.011","volume":"29","author":"NF Guler","year":"2005","unstructured":"Guler NF, Ubeyli ED, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29(3):506\u2013514","journal-title":"Expert Syst Appl"},{"issue":"6","key":"15_CR19","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","volume":"5","author":"MT Hagan","year":"1994","unstructured":"Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989\u2013993","journal-title":"IEEE Trans Neural Netw"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Wilamowski BM, Iplikci S, Kaynak O, Efe MO (2001). An algorithm for fast convergence in training neural networks. In: IJCNN\u201901. International joint conference on neural networks, proceedings (Cat. No. 01CH37222), vol 3. IEEE, Piscataway, pp 1778\u20131782","DOI":"10.1109\/IJCNN.2001.938431"},{"key":"15_CR21","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/BFb0067700","volume-title":"Numerical analysis","author":"JJ Mor\u00e9","year":"1978","unstructured":"Mor\u00e9 JJ (1978) The Levenberg-Marquardt algorithm: implementation and theory. In: Numerical analysis. Springer, Berlin, pp 105\u2013116"},{"issue":"2","key":"15_CR22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1162\/neco.1992.4.2.141","volume":"4","author":"R Battiti","year":"1992","unstructured":"Battiti R (1992) First-and second-order methods for learning: between steepest descent and newton\u2019s method. Neural Comput 4(2):141\u2013166","journal-title":"Neural Comput"},{"key":"15_CR23","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1109\/ICCV.2005.128","volume-title":"Tenth IEEE international conference on computer vision (ICCV\u201905) volume 1","author":"MLA Lourakis","year":"2005","unstructured":"Lourakis MLA, Argyros AA (2005) Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment? In: Tenth IEEE international conference on computer vision (ICCV\u201905) volume 1, vol 2. IEEE, Piscataway, pp 1526\u20131531"},{"issue":"6","key":"15_CR24","first-page":"1745","volume":"1","author":"AA Suratgar","year":"2007","unstructured":"Suratgar AA, Tavakoli MB, Hoseinabadi A (2007) Modified Levenberg-Marquardt method for neural networks training. Int J Comput Inf Eng 1(6):1745\u20131747","journal-title":"Int J Comput Inf Eng"},{"key":"15_CR25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/prot.25064","volume":"84","author":"J Moult","year":"2016","unstructured":"Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2016) Critical assessment of methods of protein structure prediction: progress and new directions in round xi. Proteins Struct Funct Bioinf 84:4\u201314","journal-title":"Proteins Struct Funct Bioinf"},{"issue":"7","key":"15_CR26","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1110\/ps.9.7.1399","volume":"9","author":"R Samudrala","year":"2000","unstructured":"Samudrala R, Levitt M (2000) Decoys 2\u0306018r2\u0306019us: a database of incorrect conformations to improve protein structure prediction. Protein Sci 9(7):1399\u20131401","journal-title":"Protein Sci"},{"issue":"4","key":"15_CR27","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1002\/prot.22193","volume":"74","author":"E Faraggi","year":"2009","unstructured":"Faraggi E, Xue B, Zhou Y (2009) Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins Struct Funct Bioinf 74(4):847\u2013856","journal-title":"Proteins Struct Funct Bioinf"},{"issue":"11","key":"15_CR28","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1016\/j.str.2009.09.006","volume":"17","author":"E Faraggi","year":"2009","unstructured":"Faraggi E, Yang Y, Zhang S, Zhou Y (2009) Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction. Structure 17(11):1515\u20131527","journal-title":"Structure"},{"issue":"3","key":"15_CR29","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1002\/jcc.21968","volume":"33","author":"E Faraggi","year":"2012","unstructured":"Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y (2012) Spine x: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 33(3):259\u2013267","journal-title":"J Comput Chem"}],"container-title":["Methods in Molecular Biology","Artificial Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-0826-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T15:24:48Z","timestamp":1696605888000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-1-0716-0826-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,18]]},"ISBN":["9781071608258","9781071608265"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-0826-5_15","relation":{},"ISSN":["1064-3745","1940-6029"],"issn-type":[{"type":"print","value":"1064-3745"},{"type":"electronic","value":"1940-6029"}],"subject":[],"published":{"date-parts":[[2020,8,18]]},"assertion":[{"value":"18 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}