{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T14:28:24Z","timestamp":1726064904793},"publisher-location":"New York, NY","reference-count":106,"publisher":"Springer US","isbn-type":[{"type":"print","value":"9781071601495"},{"type":"electronic","value":"9781071601501"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","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":[[2020]]},"DOI":"10.1007\/978-1-0716-0150-1_8","type":"book-chapter","created":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T13:29:19Z","timestamp":1579181359000},"page":"177-194","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["On the Relevance of Feature Selection Algorithms While Developing Non-linear QSARs"],"prefix":"10.1007","author":[{"given":"Riccardo","family":"Concu","sequence":"first","affiliation":[]},{"given":"M. Nat\u00e1lia Dias Soeiro","family":"Cordeiro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,17]]},"reference":[{"issue":"18","key":"8_CR1","doi-asserted-by":"publisher","first-page":"2817","DOI":"10.1021\/ja00901a033","volume":"85","author":"C Hansch","year":"1963","unstructured":"Hansch C, Muir RM, Fujita T, Maloney PP, Geiger F, Streich M (1963) The correlation of biological activity of plant growth regulators and chloromycetin derivatives with hammett constants and partition coefficients. J Am Chem Soc 85(18):2817\u20132824","journal-title":"J Am Chem Soc"},{"issue":"1","key":"8_CR2","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1016\/0045-6535(95)00119-S","volume":"31","author":"VK Gombar","year":"1995","unstructured":"Gombar VK, Enslein K, Blake BW (1995) Assessment of developmental toxicity potential of chemicals by quantitative structure-toxicity relationship models. Chemosphere 31(1):2499\u20132510","journal-title":"Chemosphere"},{"issue":"2","key":"8_CR3","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1021\/ci0342066","volume":"44","author":"K Roy","year":"2004","unstructured":"Roy K, Ghosh G (2004) QSTR with extended topochemical atom indices. 2. Fish toxicity of substituted benzenes. J Chem Inf Comput Sci 44(2):559\u2013567","journal-title":"J Chem Inf Comput Sci"},{"issue":"4","key":"8_CR4","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1021\/ci990119v","volume":"40","author":"SC Basak","year":"2000","unstructured":"Basak SC, Nikolic S, Trinajstic N, Amic D, Beslo D (2000) QSPR modeling: graph connectivity indices versus line graph connectivity indices. J Chem Inf Comput Sci 40(4):927\u2013933","journal-title":"J Chem Inf Comput Sci"},{"issue":"2","key":"8_CR5","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/S1461-5347(99)00215-1","volume":"3","author":"II Grover","year":"2000","unstructured":"Grover II, Singh II, Bakshi II (2000) Quantitative structure-property relationships in pharmaceutical research \u2013 part 2. Pharm Sci Technolo Today 3(2):50\u201357","journal-title":"Pharm Sci Technolo Today"},{"issue":"1","key":"8_CR6","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/S1461-5347(99)00214-X","volume":"3","author":"II Grover","year":"2000","unstructured":"Grover II, Singh II, Bakshi II (2000) Quantitative structure-property relationships in pharmaceutical research \u2013 part 1. Pharm Sci Technolo Today 3(1):28\u201335","journal-title":"Pharm Sci Technolo Today"},{"issue":"7","key":"8_CR7","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1080\/17435390.2017.1379567","volume":"11","author":"R Concu","year":"2017","unstructured":"Concu R, Kleandrova VV, Speck-Planche A, Cordeiro M (2017) Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology 11(7):891\u2013906","journal-title":"Nanotoxicology"},{"issue":"3","key":"8_CR8","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1002\/wnan.137","volume":"3","author":"E Burello","year":"2011","unstructured":"Burello E, Worth AP (2011) QSAR modeling of nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3(3):298\u2013306","journal-title":"Wiley Interdiscip Rev Nanomed Nanobiotechnol"},{"issue":"12","key":"8_CR9","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977\u20135010","journal-title":"J Med Chem"},{"issue":"9","key":"8_CR10","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1080\/10408444.2018.1528207","volume":"48","author":"A Wilm","year":"2018","unstructured":"Wilm A, Kuhnl J, Kirchmair J (2018) Computational approaches for skin sensitization prediction. Crit Rev Toxicol 48(9):738\u2013760","journal-title":"Crit Rev Toxicol"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1093\/ilar\/ilw031","volume":"57","author":"KA Ford","year":"2016","unstructured":"Ford KA (2016) Refinement, reduction, and replacement of animal toxicity tests by computational methods. ILAR J 57(2):226\u2013233","journal-title":"ILAR J"},{"issue":"6\u20137","key":"8_CR12","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/minf.201000061","volume":"29","author":"A Tropsha","year":"2010","unstructured":"Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29(6\u20137):476\u2013488","journal-title":"Mol Inform"},{"issue":"D1","key":"8_CR13","doi-asserted-by":"publisher","first-page":"D945","DOI":"10.1093\/nar\/gkw1074","volume":"45","author":"A Gaulton","year":"2017","unstructured":"Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):D945\u2013DD54","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"8_CR14","doi-asserted-by":"publisher","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","volume":"47","author":"S Kim","year":"2019","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S et al (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47(D1):D1102\u2013D11D9","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1021\/ed100697w","volume":"87","author":"HE Pence","year":"2010","unstructured":"Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87(11):1123\u20131124","journal-title":"J Chem Educ"},{"issue":"2","key":"8_CR16","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10822-013-9635-9","volume":"27","author":"I Jabeen","year":"2013","unstructured":"Jabeen I, Wetwitayaklung P, Chiba P, Pastor M, Ecker GF (2013) 2D- and 3D-QSAR studies of a series of benzopyranes and benzopyrano[3,4b][1,4]-oxazines as inhibitors of the multidrug transporter P-glycoprotein. J Comput Aided Mol Des 27(2):161\u2013171","journal-title":"J Comput Aided Mol Des"},{"issue":"2","key":"8_CR17","first-page":"237","volume":"56","author":"A Mauri","year":"2006","unstructured":"Mauri A, Consonni V, Pavan M, Todeschini R (2006) Dragon software: an easy approach to molecular descriptor calculations. Match Commun Math Comput Chem 56(2):237\u2013248","journal-title":"Match Commun Math Comput Chem"},{"issue":"4","key":"8_CR18","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1021\/ci00020a039","volume":"34","author":"J Sadowski","year":"1994","unstructured":"Sadowski J, Gasteiger J, Klebe G (1994) Comparison of automatic three-dimensional model builders using 639 x-ray structures. J Chem Inf Comput Sci 34(4):1000\u20131008","journal-title":"J Chem Inf Comput Sci"},{"issue":"2","key":"8_CR19","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1021\/ci0000528","volume":"41","author":"F Ignatz-Hoover","year":"2001","unstructured":"Ignatz-Hoover F, Petrukhin R, Karelson M, Katritzky AR (2001) QSRR correlation of free-radical polymerization chain-transfer constants for styrene. J Chem Inf Comput Sci 41(2):295\u2013299","journal-title":"J Chem Inf Comput Sci"},{"issue":"7","key":"8_CR20","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1016\/j.ejmech.2008.12.004","volume":"44","author":"K Roy","year":"2009","unstructured":"Roy K, Pratim RP (2009) Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G\/PLS and ANN techniques. Eur J Med Chem 44(7):2913\u20132922","journal-title":"Eur J Med Chem"},{"key":"8_CR21","first-page":"137","volume":"458","author":"II Baskin","year":"2008","unstructured":"Baskin II, Palyulin VA, Zefirov NS (2008) Neural networks in building QSAR models. Methods Mol Biol 458:137\u2013158","journal-title":"Methods Mol Biol"},{"issue":"2\u20133","key":"8_CR22","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1080\/10629369308028825","volume":"1","author":"M Wiese","year":"1993","unstructured":"Wiese M, Schaper KJ (1993) Application of neural networks in the QSAR analysis of percent effect biological data: comparison with adaptive least squares and nonlinear regression analysis. SAR QSAR Environ Res 1(2\u20133):137\u2013152","journal-title":"SAR QSAR Environ Res"},{"issue":"6","key":"8_CR23","doi-asserted-by":"publisher","first-page":"2048","DOI":"10.1021\/ci0340916","volume":"43","author":"VV Zernov","year":"2003","unstructured":"Zernov VV, Balakin KV, Ivaschenko AA, Savchuk NP, Pletnev IV (2003) Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci 43(6):2048\u20132056","journal-title":"J Chem Inf Comput Sci"},{"issue":"5\u20136","key":"8_CR24","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1080\/10629360701428474","volume":"18","author":"S Li","year":"2007","unstructured":"Li S, Fedorowicz A, Andrew ME (2007) A new descriptor selection scheme for SVM in unbalanced class problem: a case study using skin sensitisation dataset. SAR QSAR Environ Res 18(5\u20136):423\u2013441","journal-title":"SAR QSAR Environ Res"},{"issue":"1","key":"8_CR25","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.ejmech.2009.09.022","volume":"45","author":"W Shi","year":"2010","unstructured":"Shi W, Zhang X, Shen Q (2010) Quantitative structure-activity relationships studies of CCR5 inhibitors and toxicity of aromatic compounds using gene expression programming. Eur J Med Chem 45(1):49\u201354","journal-title":"Eur J Med Chem"},{"issue":"6","key":"8_CR26","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1002\/etc.2534","volume":"33","author":"IB Stoyanova-Slavova","year":"2014","unstructured":"Stoyanova-Slavova IB, Slavov SH, Pearce B, Buzatu DA, Beger RD, Wilkes JG (2014) Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data-activity relationship consensus modeling of acute toxicity. Environ Toxicol Chem 33(6):1271\u20131282","journal-title":"Environ Toxicol Chem"},{"issue":"4","key":"8_CR27","doi-asserted-by":"publisher","first-page":"622","DOI":"10.18433\/J3JK5P","volume":"16","author":"K Nikolic","year":"2013","unstructured":"Nikolic K, Filipic S, Smolinski A, Kaliszan R, Agbaba D (2013) Partial least square and hierarchical clustering in ADMET modeling: prediction of blood-brain barrier permeation of alpha-adrenergic and imidazoline receptor ligands. J Pharm Pharm Sci 16(4):622\u2013647","journal-title":"J Pharm Pharm Sci"},{"issue":"4","key":"8_CR28","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1021\/ci3000198","volume":"52","author":"S Brandmaier","year":"2012","unstructured":"Brandmaier S, Sahlin U, Tetko IV, Oberg T (2012) PLS-optimal: a stepwise D-optimal design based on latent variables. J Chem Inf Model 52(4):975\u2013983","journal-title":"J Chem Inf Model"},{"issue":"8","key":"8_CR29","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.2174\/1573406411309080005","volume":"9","author":"M Koba","year":"2013","unstructured":"Koba M, Baczek T (2013) The evaluation of multivariate adaptive regression splines for the prediction of antitumor activity of acridinone derivatives. Med Chem 9(8):1041\u20131050","journal-title":"Med Chem"},{"issue":"1\u20132","key":"8_CR30","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.chroma.2004.07.112","volume":"1055","author":"R Put","year":"2004","unstructured":"Put R, Xu QS, Massart DL, Vander HY (2004) Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure-retention relationship studies. J Chromatogr A 1055(1\u20132):11\u201319","journal-title":"J Chromatogr A"},{"issue":"32","key":"8_CR31","doi-asserted-by":"publisher","first-page":"4297","DOI":"10.2174\/092986709789578213","volume":"16","author":"T Scior","year":"2009","unstructured":"Scior T, Medina-Franco JL, Do QT, Martinez-Mayorga K, Yunes Rojas JA, Bernard P (2009) How to recognize and workaround pitfalls in QSAR studies: a critical review. Curr Med Chem 16(32):4297\u20134313","journal-title":"Curr Med Chem"},{"key":"8_CR32","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-1-62703-059-5_21","volume":"930","author":"P Gramatica","year":"2013","unstructured":"Gramatica P (2013) On the development and validation of QSAR models. Methods Mol Biol 930:499\u2013526","journal-title":"Methods Mol Biol"},{"issue":"1","key":"8_CR33","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1021\/ci050215y","volume":"46","author":"SC Basak","year":"2006","unstructured":"Basak SC, Natarajan R, Mills D, Hawkins DM, Kraker JJ (2006) Quantitative structure-activity relationship modeling of juvenile hormone mimetic compounds for Culex pipiens larvae, with a discussion of descriptor-thinning methods. J Chem Inf Model 46(1):65\u201377","journal-title":"J Chem Inf Model"},{"issue":"12","key":"8_CR34","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1080\/17460441.2018.1542428","volume":"13","author":"PM Khan","year":"2018","unstructured":"Khan PM, Roy K (2018) Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR). Expert Opin Drug Dis 13(12):1075\u20131089","journal-title":"Expert Opin Drug Dis"},{"issue":"9","key":"8_CR35","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1021\/ci800151m","volume":"48","author":"IV Tetko","year":"2008","unstructured":"Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E et al (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 48(9):1733\u20131746","journal-title":"J Chem Inf Model"},{"issue":"10","key":"8_CR36","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1021\/jm00280a002","volume":"15","author":"JG Topliss","year":"1972","unstructured":"Topliss JG (1972) Utilization of operational schemes for analog synthesis in drug design. J Med Chem 15(10):1006\u20131011","journal-title":"J Med Chem"},{"issue":"1","key":"8_CR37","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3233\/IDA-1997-1302","volume":"1","author":"M Dash","year":"1997","unstructured":"Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1):131\u2013156","journal-title":"Intell Data Anal"},{"key":"8_CR38","unstructured":"Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. Proceedings of the tenth national conference on artificial intelligence, San Jose, 1867155, AAAI Press, pp 129\u2013134"},{"issue":"9","key":"8_CR39","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/TC.1977.1674939","volume":"26","author":"PM Narendra","year":"1977","unstructured":"Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 26(9):917\u2013922","journal-title":"IEEE Trans Comput"},{"key":"8_CR40","unstructured":"Koller D, Sahami M (1996) Toward optimal feature selection. Proceedings of the thirteenth international conference on machine learning, Bari, 3091731, Morgan Kaufmann Publishers Inc., pp 284\u2013292"},{"issue":"1","key":"8_CR41","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/S0004-3702(03)00079-1","volume":"151","author":"M Dash","year":"2003","unstructured":"Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1):155\u2013176","journal-title":"Artif Intell"},{"issue":"3","key":"8_CR42","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10844-007-0037-0","volume":"30","author":"A Arauzo-Azofra","year":"2008","unstructured":"Arauzo-Azofra A, Benitez JM, Castro JL (2008) Consistency measures for feature selection. J Intell Inf Syst 30(3):273\u2013292","journal-title":"J Intell Inf Syst"},{"issue":"12","key":"8_CR43","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/34.643896","volume":"19","author":"BH Jun","year":"1997","unstructured":"Jun BH, Kim CS, Song H, Kim J (1997) A new criterion in selection and discretization of attributes for the generation of decision trees. IEEE Trans Pattern Anal Mach Intell 19(12):1371\u20131375","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"8_CR44","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comp Electr Eng 40(1):16\u201328","journal-title":"Comp Electr Eng"},{"issue":"2","key":"8_CR45","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/S0377-2217(02)00911-6","volume":"156","author":"S Piramuthu","year":"2004","unstructured":"Piramuthu S (2004) Evaluating feature selection methods for learning in data mining applications. Eur J Oper Res 156(2):483\u2013494","journal-title":"Eur J Oper Res"},{"issue":"5","key":"8_CR46","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1021\/ci000384c","volume":"40","author":"DC Whitley","year":"2000","unstructured":"Whitley DC, Ford MG, Livingstone DJ (2000) Unsupervised forward selection: a method for eliminating redundant variables. J Chem Inf Comput Sci 40(5):1160\u20131168","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"8_CR47","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1006\/mchj.1993.1012","volume":"47","author":"JM Sutter","year":"1993","unstructured":"Sutter JM, Kalivas JH (1993) Comparison of forward selection, backward elimination, and generalized simulated annealing for variable selection. Microchem J 47(1):60\u201366","journal-title":"Microchem J"},{"key":"8_CR48","unstructured":"Livingstone DJ, Salt DW (2005) Variable selection\u2014Spoilt for choice? Reviews in Computational Chemistry, Ed. Lipkowitz KB, Larter R, Cundari TR, John Wiley & Sons, Inc., chap.4, vol 21, pp. 287\u2013348"},{"key":"8_CR49","unstructured":"Almuallim H, Dietterich TG (1991) Learning with many irrelevant features. Proceedings of the ninth National conference on Artificial intelligence, vol 2, Anaheim, 1865761, AAAI Press, pp 547\u2013552"},{"issue":"1","key":"8_CR50","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/0004-3702(94)90084-1","volume":"69","author":"H Almuallim","year":"1994","unstructured":"Almuallim H, Dietterich TG (1994) Learning Boolean concepts in the presence of many irrelevant features. Artif Intell 69(1):279\u2013305","journal-title":"Artif Intell"},{"key":"8_CR51","doi-asserted-by":"crossref","unstructured":"Arauzo A, Ben\u00edtez JM, Castro JL (eds) C-FOCUS: a continuous extension of FOCUS2003. Springer, London","DOI":"10.1007\/978-1-4471-3744-3_22"},{"issue":"3","key":"8_CR52","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TKDE.2002.1000349","volume":"14","author":"FEH Tay","year":"2002","unstructured":"Tay FEH, Lixiang S (2002) A modified Chi2 algorithm for discretization. IEEE Trans Knowl Data Eng 14(3):666\u2013670","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"8_CR53","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/69.842268","volume":"12","author":"E Boros","year":"2000","unstructured":"Boros E, Hammer PL, Ibaraki T, Kogan A, Mayoraz E, Muchnik I (2000) An implementation of logical analysis of data. IEEE Trans Knowl Data Eng 12(2):292\u2013306","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8_CR54","doi-asserted-by":"crossref","unstructured":"Dem\u0161ar J, Zupan B, Leban G, Curk T (eds) Orange: from experimental machine learning to interactive data mining 2004. Springer Berlin Heidelberg, Berlin, Heidelberg","DOI":"10.1007\/978-3-540-30116-5_58"},{"issue":"2","key":"8_CR55","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1023\/A:1007612503587","volume":"41","author":"DA Bell","year":"2000","unstructured":"Bell DA, Wang H (2000) A formalism for relevance and its application in feature subset selection. Mach Learn 41(2):175\u2013195","journal-title":"Mach Learn"},{"key":"8_CR56","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/B978-1-55860-307-3.50010-1","volume-title":"Machine Learning Proceedings 1993","author":"Claire Cardie","year":"1993","unstructured":"Cardie C (1993) Using decision trees to improve case-based learning, in machine learning proceedings. Morgan Kaufmann, San Francisco (CA), pp 25\u201332"},{"issue":"8","key":"8_CR57","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"P Hanchuan","year":"2005","unstructured":"Hanchuan P, Fuhui L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6769","key":"8_CR58","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/35000501","volume":"403","author":"AA Alizadeh","year":"2000","unstructured":"Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503\u2013511","journal-title":"Nature"},{"issue":"2","key":"8_CR59","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/34.574797","volume":"19","author":"A Jain","year":"1997","unstructured":"Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153\u2013158","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8_CR60","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1038\/73432","volume":"24","author":"DT Ross","year":"2000","unstructured":"Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P et al (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 24:227","journal-title":"Nat Genet"},{"key":"8_CR61","doi-asserted-by":"crossref","unstructured":"Ding C, Peng H (eds) (2003) Minimum redundancy feature selection from microarray gene expression data. Computational systems bioinformatics CSB2003 proceedings of the 2003 IEEE bioinformatics conference CSB2003, 11\u201314 Aug 2003","DOI":"10.1109\/CSB.2003.1227396"},{"key":"8_CR62","doi-asserted-by":"crossref","unstructured":"Claypo N, Jaiyen S (eds) (2015) A new feature selection based on class dependency and feature dissimilarity. 2015 2nd international conference on advanced informatics: concepts, theory and applications (ICAICTA), 19\u201322 Aug 2015","DOI":"10.1109\/ICAICTA.2015.7335366"},{"key":"8_CR63","doi-asserted-by":"crossref","unstructured":"Yu-Shuen T, Ueng-Cheng Y, Chung IF, Chuen-Der H (eds) (2013) A comparison of mutual and fuzzy-mutual information-based feature selection strategies. 2013 IEEE international conference on fuzzy systems (FUZZ-IEEE), 7\u201310 July 2013","DOI":"10.1109\/FUZZ-IEEE.2013.6622533"},{"key":"8_CR64","doi-asserted-by":"crossref","unstructured":"Cheng Q, Zhou H, Cheng J (2011) The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data 2011, pp 1217\u20131233","DOI":"10.1109\/TPAMI.2010.195"},{"issue":"5","key":"8_CR65","first-page":"476","volume":"19","author":"S Aalaei","year":"2016","unstructured":"Aalaei S, Shahraki H, Rowhanimanesh A, Eslami S (2016) Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets. Iran J Basic Med Sci 19(5):476\u2013482","journal-title":"Iran J Basic Med Sci"},{"key":"8_CR66","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/B978-0-08-047865-4.50016-8","volume-title":"Introduction to statistical pattern recognition","author":"K Fukunaga","year":"1990","unstructured":"Fukunaga K (1990) Chapter 10 \u2013 feature extraction and linear mapping for classification. In: Fukunaga K (ed) Introduction to statistical pattern recognition, 2nd edn. Academic Press, Boston, pp 441\u2013507","edition":"2"},{"key":"8_CR67","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/B978-0-08-047865-4.50015-6","volume-title":"Introduction to statistical pattern recognition","author":"K Fukunaga","year":"1990","unstructured":"Fukunaga K (1990) Chapter 9 \u2013 feature extraction and linear mapping for signal representation. In: Fukunaga K (ed) Introduction to statistical pattern recognition, 2nd edn. Academic Press, Boston, pp 399\u2013440","edition":"2"},{"issue":"8","key":"8_CR68","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1016\/S0031-3203(03)00035-9","volume":"36","author":"E Choi","year":"2003","unstructured":"Choi E, Lee C (2003) Feature extraction based on the Bhattacharyya distance. Pattern Recogn 36(8):1703\u20131709","journal-title":"Pattern Recogn"},{"key":"8_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2015.08.010","volume":"66","author":"P Drot\u00e1r","year":"2015","unstructured":"Drot\u00e1r P, Gazda J, Sm\u00e9kal Z (2015) An experimental comparison of feature selection methods on two-class biomedical datasets. Comput Biol Med 66:1\u201310","journal-title":"Comput Biol Med"},{"issue":"1","key":"8_CR70","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389\u2013422","journal-title":"Mach Learn"},{"issue":"1\u20132","key":"8_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/cem.971","volume":"20","author":"Y Akhlaghi","year":"2006","unstructured":"Akhlaghi Y, Kompany-Zareh M (2006) Application of radial basis function networks and successive projections algorithm in a QSAR study of anti-HIV activity for a large group of HEPT derivatives. J Chemom 20(1\u20132):1\u201312","journal-title":"J Chemom"},{"issue":"10","key":"8_CR72","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1016\/j.neucom.2009.11.045","volume":"73","author":"T Shanableh","year":"2010","unstructured":"Shanableh T, Assaleh K (2010) Feature modeling using polynomial classifiers and stepwise regression. Neurocomputing 73(10):1752\u20131759","journal-title":"Neurocomputing"},{"issue":"1","key":"8_CR73","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1):273\u2013324","journal-title":"Artif Intell"},{"key":"8_CR74","doi-asserted-by":"crossref","unstructured":"Naseriparsa M, Bidgoli A-M, Varaee T (2013) A hybrid feature selection method to improve performance of a group of classification algorithms. CoRR;abs\/1403.2372","DOI":"10.5120\/12065-8172"},{"issue":"1","key":"8_CR75","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1021\/ci050293l","volume":"46","author":"O Nicolotti","year":"2006","unstructured":"Nicolotti O, Carotti A (2006) QSAR and QSPR studies of a highly structured physicochemical domain. J Chem Inf Model 46(1):264\u2013276","journal-title":"J Chem Inf Model"},{"key":"8_CR76","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-1-4615-5725-8_8","volume-title":"Feature extraction, construction and selection: a data mining perspective","author":"J Yang","year":"1998","unstructured":"Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. In: Liu H, Motoda H (eds) Feature extraction, construction and selection: a data mining perspective. Springer US, Boston, pp 117\u2013136"},{"issue":"5","key":"8_CR77","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1080\/10629360600933723","volume":"17","author":"XZ Wang","year":"2006","unstructured":"Wang XZ, Buontempo FV, Young A, Osborn D (2006) Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints. SAR QSAR Environ Res 17(5):451\u2013471","journal-title":"SAR QSAR Environ Res"},{"issue":"1","key":"8_CR78","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1111\/j.1747-0285.2010.01044.x","volume":"77","author":"CD Fjell","year":"2011","unstructured":"Fjell CD, Jenssen H, Cheung WA, Hancock RE, Cherkasov A (2011) Optimization of antibacterial peptides by genetic algorithms and cheminformatics. Chem Biol Drug Des 77(1):48\u201356","journal-title":"Chem Biol Drug Des"},{"key":"8_CR79","doi-asserted-by":"crossref","unstructured":"Kumar M, Husain M, Upreti N, Gupta D (2010) Genetic algorithm: review and application. IJITM 2(2):451\u2013454","DOI":"10.2139\/ssrn.3529843"},{"issue":"3","key":"8_CR80","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1109\/8.558650","volume":"45","author":"DS Weile","year":"1997","unstructured":"Weile DS, Michielssen E (1997) Genetic algorithm optimization applied to electromagnetics: a review. IEEE Trans Antennas Propag 45(3):343\u2013353","journal-title":"IEEE Trans Antennas Propag"},{"volume-title":"Application of genetic algorithms to packing problems \u2014 a review","year":"1998","key":"8_CR81","unstructured":"Hopper E, Turton B (eds) (1998) Application of genetic algorithms to packing problems \u2014 a review. Springer, London"},{"key":"8_CR82","doi-asserted-by":"crossref","unstructured":"Hussein F, Kharma N, Ward R (eds) (2001) Genetic algorithms for feature selection and weighting, a review and study. Proceedings of Sixth International Conference on Document Analysis and Recognition. 13 Sept 2001","DOI":"10.1109\/ICDAR.2001.953980"},{"issue":"7","key":"8_CR83","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1002\/cem.651","volume":"15","author":"R Leardi","year":"2001","unstructured":"Leardi R (2001) Genetic algorithms in chemometrics and chemistry: a review. J Chemom 15(7):559\u2013569","journal-title":"J Chemom"},{"issue":"1","key":"8_CR84","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s11030-010-9234-9","volume":"15","author":"M Fernandez","year":"2011","unstructured":"Fernandez M, Caballero J, Fernandez L, Sarai A (2011) Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers 15(1):269\u2013289","journal-title":"Mol Divers"},{"issue":"1","key":"8_CR85","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/S0166-1280(02)00619-X","volume":"622","author":"SP Niculescu","year":"2003","unstructured":"Niculescu SP (2003) Artificial neural networks and genetic algorithms in QSAR. J Mol Struct THEOCHEM 622(1):71\u201383","journal-title":"J Mol Struct THEOCHEM"},{"issue":"5","key":"8_CR86","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1021\/ci049933v","volume":"44","author":"V Venkatraman","year":"2004","unstructured":"Venkatraman V, Dalby AR, Yang ZR (2004) Evaluation of mutual information and genetic programming for feature selection in QSAR. J Chem Inf Comput Sci 44(5):1686\u20131692","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"8_CR87","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.swevo.2011.03.001","volume":"1","author":"A Zhou","year":"2011","unstructured":"Zhou A, Qu B-Y, Li H, Zhao S-Z, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolutionary Comput 1(1):32\u201349","journal-title":"Swarm Evolutionary Comput"},{"key":"8_CR88","doi-asserted-by":"crossref","unstructured":"Ozdemir M, Embrechts MJ, Arciniegas F, Breneman CM, Lockwood L, Bennett KP (eds) (2001) Feature selection for in-silico drug design using genetic algorithms and neural networks. SMCia\/01 proceedings of the 2001 IEEE mountain workshop on soft computing in industrial applications (Cat No01EX504), 27 June 2001","DOI":"10.1109\/SMCIA.2001.936728"},{"issue":"4","key":"8_CR89","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s10337-017-3273-7","volume":"80","author":"A Bahmani","year":"2017","unstructured":"Bahmani A, Saaidpour S, Rostami A (2017) Quantitative structure\u2013retention relationship modeling of morphine and its derivatives on OV-1 column in gas\u2013liquid chromatography using genetic algorithm. Chromatographia 80(4):629\u2013636","journal-title":"Chromatographia"},{"key":"8_CR90","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.talanta.2016.11.041","volume":"164","author":"M Mizera","year":"2017","unstructured":"Mizera M, Krause A, Zalewski P, Skibi\u0144ski R, Cielecka-Piontek J (2017) Quantitative structure-retention relationship model for the determination of naratriptan hydrochloride and its impurities based on artificial neural networks coupled with genetic algorithm. Talanta 164:164\u2013174","journal-title":"Talanta"},{"key":"8_CR91","doi-asserted-by":"publisher","first-page":"S185","DOI":"10.1016\/j.arabjc.2011.03.006","volume":"9","author":"G Ghasemi","year":"2016","unstructured":"Ghasemi G, Nirouei M, Shariati S, Abdolmaleki P, Rastgoo Z (2016) A quantitative structure\u2013activity relationship study on HIV-1 integrase inhibitors using genetic algorithm, artificial neural networks and different statistical methods. Arab J Chem 9:S185\u2013SS90","journal-title":"Arab J Chem"},{"key":"8_CR92","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.powtec.2016.01.028","volume":"292","author":"A Vel\u00e1sco-Mej\u00eda","year":"2016","unstructured":"Vel\u00e1sco-Mej\u00eda A, Vallejo-Becerra V, Ch\u00e1vez-Ram\u00edrez AU, Torres-Gonz\u00e1lez J, Reyes-Vidal Y, Casta\u00f1eda-Zaldivar F (2016) Modeling and optimization of a pharmaceutical crystallization process by using neural networks and genetic algorithms. Powder Technol 292:122\u2013128","journal-title":"Powder Technol"},{"key":"8_CR93","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.ejpb.2015.04.028","volume":"94","author":"Y Li","year":"2015","unstructured":"Li Y, Abbaspour MR, Grootendorst PV, Rauth AM, Wu XY (2015) Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology. Eur J Pharm Biopharm 94:170\u2013179","journal-title":"Eur J Pharm Biopharm"},{"key":"8_CR94","doi-asserted-by":"publisher","first-page":"2680","DOI":"10.1007\/s00044-011-9794-y","volume":"21","author":"H Noorizadeh","year":"2011","unstructured":"Noorizadeh H, Farmany A, Noorizadeh M (2011) Application of GA\u2013KPLS and L\u2013M ANN calculations for the prediction of the capacity factor of hazardous psychoactive designer drugs. Med Chem Res 21:2680\u20132688","journal-title":"Med Chem Res"},{"key":"8_CR95","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/978-3-319-06508-3_13","volume-title":"Applications of metaheuristics in process engineering","author":"N Sukumar","year":"2014","unstructured":"Sukumar N, Prabhu G, Saha P (2014) Applications of genetic algorithms in QSAR\/QSPR modeling. In: Valadi J, Siarry P (eds) Applications of metaheuristics in process engineering. Springer International Publishing, Cham, pp 315\u2013324"},{"issue":"1","key":"8_CR96","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/3477.484436","volume":"26","author":"M Dorigo","year":"1996","unstructured":"Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29\u201341","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"issue":"6","key":"8_CR97","doi-asserted-by":"publisher","first-page":"9608","DOI":"10.1016\/j.eswa.2009.01.020","volume":"36","author":"RJ Mullen","year":"2009","unstructured":"Mullen RJ, Monekosso D, Barman S, Remagnino P (2009) A review of ant algorithms. Expert Syst Appl 36(6):9608\u20139617","journal-title":"Expert Syst Appl"},{"issue":"4","key":"8_CR98","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1021\/ci9000103","volume":"49","author":"M Goodarzi","year":"2009","unstructured":"Goodarzi M, Freitas MP, Jensen R (2009) Feature selection and linear\/nonlinear regression methods for the accurate prediction of glycogen synthase kinase-3 beta inhibitory activities. J Chem Inf Model 49(4):824\u2013832","journal-title":"J Chem Inf Model"},{"issue":"7","key":"8_CR99","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1111\/j.1745-7254.2007.00573.x","volume":"28","author":"B Niu","year":"2007","unstructured":"Niu B, Lu W-C, Yang S-S, Cai Y-D, Li G-Z (2007) Support vector machine for SAR\/QSAR of phenethyl-amines1. Acta Pharmacol Sin 28(7):1075\u20131086","journal-title":"Acta Pharmacol Sin"},{"key":"8_CR100","doi-asserted-by":"crossref","unstructured":"Embrechts MJ, Arciniegas F, Ozdemir M, Breneman CM, Bennett K, Lockwood L (eds) (2001) Bagging neural network sensitivity analysis for feature reduction for in-silico drug design. IJCNN\u201901 international joint conference on neural networks proceedings (Cat No01CH37222), 15\u201319 July 2001","DOI":"10.1109\/IJCNN.2001.938756"},{"issue":"7","key":"8_CR101","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1080\/1062936X.2012.762425","volume":"24","author":"K Tanabe","year":"2013","unstructured":"Tanabe K, Kurita T, Nishida K, Lu\u010di\u0107 B, Ami\u0107 D, Suzuki T (2013) Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models. SAR QSAR Environ Res 24(7):565\u2013580","journal-title":"SAR QSAR Environ Res"},{"key":"8_CR102","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (eds) (1995) Particle swarm optimization. Proceedings of ICNN\u201995 \u2013 international conference on neural networks. 27 Nov\u20131 Dec. 1995","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"5","key":"8_CR103","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1021\/jm0104668","volume":"45","author":"DK Agrafiotis","year":"2002","unstructured":"Agrafiotis DK, Cede\u00f1o W (2002) Feature selection for structure\u2212activity correlation using binary particle swarms. J Med Chem 45(5):1098\u20131107","journal-title":"J Med Chem"},{"key":"8_CR104","unstructured":"Wang Z, Durst GL, Eberhart RC, Boyd DB, Miled ZB (eds) Particle swarm optimization and neural network application for QSAR. 18th international parallel and distributed processing symposium, 2004 proceedings, 26\u201330 Apr 2004"},{"issue":"5","key":"8_CR105","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1021\/ci049869h","volume":"44","author":"Y Xue","year":"2004","unstructured":"Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, Chen YZ (2004) Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J Chem Inf Comput Sci 44(5):1630\u20131638","journal-title":"J Chem Inf Comput Sci"},{"issue":"11\u201312","key":"8_CR106","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1002\/qsar.200960053","volume":"28","author":"AJ Soto","year":"2009","unstructured":"Soto AJ, Cecchini RL, Vazquez GE, Ponzoni I (2009) Multi-objective feature selection in QSAR using a machine learning approach. QSAR Comb Sci 28(11\u201312):1509\u20131523","journal-title":"QSAR Comb Sci"}],"container-title":["Methods in Pharmacology and Toxicology","Ecotoxicological QSARs"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-0150-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T06:01:12Z","timestamp":1695621672000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-1-0716-0150-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9781071601495","9781071601501"],"references-count":106,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-0150-1_8","relation":{},"ISSN":["1557-2153","1940-6053"],"issn-type":[{"type":"print","value":"1557-2153"},{"type":"electronic","value":"1940-6053"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"17 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}