{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:49:25Z","timestamp":1740098965126,"version":"3.37.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319697741"},{"type":"electronic","value":"9783319697758"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-69775-8_4","type":"book-chapter","created":{"date-parts":[[2017,10,28]],"date-time":"2017-10-28T00:23:14Z","timestamp":1509150194000},"page":"67-88","source":"Crossref","is-referenced-by-count":1,"title":["Better Interpretable Models for Proteomics Data Analysis Using Rule-Based Mining"],"prefix":"10.1007","author":[{"given":"Fahrnaz","family":"Jayrannejad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim O. F.","family":"Conrad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,29]]},"reference":[{"key":"4_CR1","first-page":"774","volume":"24","author":"V Vapnik","year":"1963","unstructured":"Vapnik, V.: Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774\u2013780 (1963)","journal-title":"Autom. Remote Control"},{"key":"4_CR2","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9, 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR3","unstructured":"Helleputte, T.: LiblineaR: Linear Predictive Models Based on the LIBLINEAR C\/C++ Library. R package version 2.10-8 (2017)"},{"issue":"5","key":"4_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v039.i05","volume":"39","author":"N Simon","year":"2011","unstructured":"Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for cox\u2019s proportional hazards model via coordinate descent. J. Stat. Softw. 39(5), 1\u201313 (2011)","journal-title":"J. Stat. Softw."},{"issue":"1","key":"4_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1\u201322 (2010)","journal-title":"J. Stat. Softw."},{"key":"4_CR6","unstructured":"Therneau, T., Beth Atkinson, B.R.: Recursive Partitioning and Regression Trees. R package version 4.1-10 (2015)"},{"key":"4_CR7","unstructured":"Kuhn, M.: Classification and Regression Training. R package version 6.0-73 (2016)"},{"issue":"9","key":"4_CR8","doi-asserted-by":"crossref","first-page":"8228","DOI":"10.1016\/j.eswa.2012.01.141","volume":"39","author":"R Vimieiro","year":"2012","unstructured":"Vimieiro, R., Moscato, P.: Mining disjunctive minimal generators with titanicor. Expert Syst. Appl. 39(9), 8228\u20138238 (2012)","journal-title":"Expert Syst. Appl."},{"key":"4_CR9","unstructured":"Gibb, S., Strimmer, K.: Multi-Class Discriminant Analysis using Binary Predictors. R package version 1.0.3 (2015)"},{"issue":"2","key":"4_CR10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40708-016-0042-6","volume":"3","author":"A Holzinger","year":"2016","unstructured":"Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf. 3(2), 119\u2013131 (2016)","journal-title":"Brain Inf."},{"key":"4_CR11","unstructured":"Holzinger, A., Plass, M., Holzinger, K., Crisan, G.C., Pintea, C.M., Palade, V.: A glass-box interactive machine learning approach for solving np-hard problems with the human-in-the-loop. arXiv preprint (2017). \narXiv:1708.01104"},{"key":"4_CR12","unstructured":"Bakin, S., et al.: Adaptive regression and model selection in data mining problems. Ph.D. thesis, The Australian National University (1999)"},{"issue":"3","key":"4_CR13","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/00401706.1971.10488823","volume":"13","author":"WH Lawton","year":"1971","unstructured":"Lawton, W.H., Sylvestre, E.A.: Self modeling curve resolution. Technometrics 13(3), 617\u2013633 (1971)","journal-title":"Technometrics"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Loekito, E., Bailey, J.: Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 307\u2013316. ACM (2006)","DOI":"10.1145\/1150402.1150438"},{"key":"4_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.is.2013.09.001","volume":"40","author":"R Vimieiro","year":"2014","unstructured":"Vimieiro, R., Moscato, P.: A new method for mining disjunctive emerging patterns in high-dimensional datasets using hypergraphs. Inf. Syst. 40, 1\u201310 (2014)","journal-title":"Inf. Syst."},{"key":"4_CR16","unstructured":"Vimieiro, R.: Mining disjunctive patterns in biomedical data sets. Ph.D. thesis, University of Newcastle, Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science (2012)"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zaki, M.J., Ramakrishnan, N.: Blosom: a framework for mining arbitrary boolean expressions. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 827\u2013832. ACM (2006)","DOI":"10.1145\/1150402.1150511"},{"issue":"1","key":"4_CR18","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1186\/1471-2164-10-S1-S3","volume":"10","author":"Q Liu","year":"2009","unstructured":"Liu, Q., Sung, A.H., Qiao, M., Chen, Z., Yang, J.Y., Yang, M.Q., Huang, X., Deng, Y.: Comparison of feature selection and classification for maldi-ms data. BMC Genom. 10(1), S3 (2009)","journal-title":"BMC Genom."},{"issue":"12","key":"4_CR19","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1089\/omi.2013.0017","volume":"17","author":"AL Swan","year":"2013","unstructured":"Swan, A.L., Mobasheri, A., Allaway, D., Liddell, S., Bacardit, J.: Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. Omics: J. Integr. Biol. 17(12), 595\u2013610 (2013)","journal-title":"Omics: J. Integr. Biol."},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imieli\u0144ski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM Sigmod Record, vol. 22, pp. 207\u2013216. ACM (1993)","DOI":"10.1145\/170035.170072"},{"issue":"6","key":"4_CR21","doi-asserted-by":"crossref","first-page":"e68","DOI":"10.1371\/journal.pcbi.0020068","volume":"2","author":"V Varadan","year":"2006","unstructured":"Varadan, V., Anastassiou, D.: Inference of disease-related molecular logic from systems-based microarray analysis. PLoS Comput. Biol. 2(6), e68 (2006)","journal-title":"PLoS Comput. Biol."},{"issue":"10","key":"4_CR22","doi-asserted-by":"crossref","first-page":"R157","DOI":"10.1186\/gb-2008-9-10-r157","volume":"9","author":"D Sahoo","year":"2008","unstructured":"Sahoo, D., Dill, D.L., Gentles, A.J., Tibshirani, R., Plevritis, S.K.: Boolean implication networks derived from large scale, whole genome microarray datasets. Genome Biol. 9(10), R157 (2008)","journal-title":"Genome Biol."},{"key":"4_CR23","first-page":"409","volume":"I","author":"J Li","year":"2006","unstructured":"Li, J., Li, H., Wong, L., Pei, J., Dong, G.: Minimum description length principle: Generators are preferable to closed patterns. AAA I, 409\u2013414 (2006)","journal-title":"AAA"},{"issue":"17","key":"4_CR24","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1093\/bioinformatics\/bts447","volume":"28","author":"S Gibb","year":"2012","unstructured":"Gibb, S., Strimmer, K.: MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28(17), 2270\u20132271 (2012)","journal-title":"Bioinformatics"},{"issue":"8","key":"4_CR25","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","volume":"36","author":"A Savitzky","year":"1964","unstructured":"Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627\u20131639 (1964)","journal-title":"Anal. Chem."},{"key":"4_CR26","first-page":"65","volume":"10","author":"QP He","year":"2011","unstructured":"He, Q.P., Wang, J., Mobley, J.A., Richman, J., Grizzle, W.E.: Self-calibrated warping for mass spectra alignment. Cancer Inf. 10, 65 (2011)","journal-title":"Cancer Inf."},{"key":"4_CR27","unstructured":"Fayyad, U., Irani, K.: Multi-interval discretization of continuous valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022\u20131029 (1993)"},{"key":"4_CR28","unstructured":"Kim, H.: Data preprocessing, discretization for classification. R package version 1.0-1 (2010)"},{"issue":"5","key":"4_CR29","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465\u2013471 (1978)","journal-title":"Automatica"},{"issue":"2","key":"4_CR30","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0169-023X(02)00057-5","volume":"42","author":"G Stumme","year":"2002","unstructured":"Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with titanic. Data Knowl. Eng. 42(2), 189\u2013222 (2002)","journal-title":"Data Knowl. Eng."},{"key":"4_CR31","unstructured":"Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings 20th International Conference Very Large Data Bases, VLDB, vol. 1215, pp. 487\u2013499 (1994)"},{"key":"4_CR32","unstructured":"Li, J.: Prediction by collective likelihood from emerging patterns, US Patent Ap. 10\/524,606, 22 August 2002"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43\u201352. ACM (1999)","DOI":"10.1145\/312129.312191"},{"issue":"11","key":"4_CR34","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1158\/1078-0432.CCR-08-2701","volume":"15","author":"GM Fiedler","year":"2009","unstructured":"Fiedler, G.M., Leichtle, A.B., Kase, J., Baumann, S., Ceglarek, U., Felix, K., Conrad, T., Witzigmann, H., Weimann, A., Sch\u00fctte, C., et al.: Serum peptidome profiling revealed platelet factor 4 as a potential discriminating peptide associated with pancreatic cancer. Clin. Cancer Res. 15(11), 3812\u20133819 (2009)","journal-title":"Clin. Cancer Res."},{"issue":"1","key":"4_CR35","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/s12859-017-1565-4","volume":"18","author":"TO Conrad","year":"2017","unstructured":"Conrad, T.O., Genzel, M., Cvetkovic, N., Wulkow, N., Leichtle, A., Vybiral, J., Kutyniok, G., Sch\u00fctte, C.: Sparse proteomics analysis-a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. BMC Bioinf. 18(1), 160 (2017)","journal-title":"BMC Bioinf."}],"container-title":["Lecture Notes in Computer Science","Towards Integrative Machine Learning and Knowledge Extraction"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-69775-8_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,10,28]],"date-time":"2017-10-28T00:25:35Z","timestamp":1509150335000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-69775-8_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319697741","9783319697758"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-69775-8_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}