{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:16:15Z","timestamp":1774667775115,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T00:00:00Z","timestamp":1579651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002261","name":"Russian Foundation for Basic Research","doi-asserted-by":"publisher","award":["19-29-01108"],"award-info":[{"award-number":["19-29-01108"]}],"id":[{"id":"10.13039\/501100002261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial na\u00efve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41\u2013235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61\u20130.88 range to 0.70\u20130.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.<\/jats:p>","DOI":"10.3390\/ijms21030713","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T11:17:57Z","timestamp":1579691877000},"page":"713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology"],"prefix":"10.3390","volume":"21","author":[{"given":"Victor","family":"Tkachev","sequence":"first","affiliation":[{"name":"OmicsWayCorp, Walnut, CA 91788, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxim","family":"Sorokin","sequence":"additional","affiliation":[{"name":"OmicsWayCorp, Walnut, CA 91788, USA"},{"name":"Institute for Personailzed Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Constantin","family":"Borisov","sequence":"additional","affiliation":[{"name":"National Research University\u2014Higher School of Economics, 101000 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Garazha","sequence":"additional","affiliation":[{"name":"OmicsWayCorp, Walnut, CA 91788, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anton","family":"Buzdin","sequence":"additional","affiliation":[{"name":"OmicsWayCorp, Walnut, CA 91788, USA"},{"name":"Institute for Personailzed Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia"},{"name":"Moscow Institute of Physics and Technology, 141701 Moscow Oblast, Russia"},{"name":"Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-5524","authenticated-orcid":false,"given":"Nicolas","family":"Borisov","sequence":"additional","affiliation":[{"name":"OmicsWayCorp, Walnut, CA 91788, USA"},{"name":"Institute for Personailzed Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia"},{"name":"Moscow Institute of Physics and Technology, 141701 Moscow Oblast, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.semcancer.2018.06.003","article-title":"Molecular pathway activation\u2014New type of biomarkers for tumor morphology and personalized selection of target drugs","volume":"53","author":"Buzdin","year":"2018","journal-title":"Semin. Cancer Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1134\/S000629790805012X","article-title":"Targeted therapy in the treatment of solid tumors: Practice contradicts theory","volume":"73","author":"Zhukov","year":"2008","journal-title":"Biochem. Biokhimiia"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Buzdin, A., Sorokin, M., Garazha, A., Glusker, A., Aleshin, A., Poddubskaya, E., Sekacheva, M., Kim, E., Gaifullin, N., and Giese, A. (2019). RNA sequencing for research and diagnostics in clinical oncology. Semin. Cancer Biol.","DOI":"10.1016\/j.semcancer.2019.07.010"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29347","DOI":"10.18632\/oncotarget.5119","article-title":"A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation","volume":"6","author":"Artemov","year":"2015","journal-title":"Oncotarget"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"656","DOI":"10.18632\/oncotarget.6394","article-title":"Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human","volume":"7","author":"Shepelin","year":"2016","journal-title":"Oncotarget"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fphar.2019.00001","article-title":"Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs","volume":"10","author":"Zolotovskaia","year":"2019","journal-title":"Front. Pharmacol."},{"key":"ref_7","first-page":"1176935119838844","article-title":"High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation Analysis Approach in Cancer Biology","volume":"18","author":"Buzdin","year":"2019","journal-title":"Cancer Inf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3389\/fgene.2018.00717","article-title":"FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier","volume":"9","author":"Tkachev","year":"2019","journal-title":"Front. Genet."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bartlett, P., and Shawe-Taylor, J. (1999). Generalization performance of support vector machines and other pattern classifiers. Advances in Kernel Methods: Support Vector Learning, MIT Press.","DOI":"10.7551\/mitpress\/1130.003.0007"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1586\/epr.09.83","article-title":"Bioinformatics for protein biomarker panel classification: What is needed to bring biomarker panels into in vitro diagnostics?","volume":"6","author":"Robin","year":"2009","journal-title":"Expert Rev. Proteomics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1093\/bioinformatics\/btr300","article-title":"Classification with correlated features: Unreliability of feature ranking and solutions","volume":"27","author":"Lengauer","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_12","unstructured":"Stigler, S.M. (1986). The History of Statistics: The Measurement of Uncertainty Before 1900, Belknap Press of Harvard University Press."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cramer, J.S. (2003). The Origins of Logistic Regression, Tinbergen Institute. Tinbergen Institute Working Paper No. 2002-119\/4.","DOI":"10.2139\/ssrn.360300"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1137\/0907087","article-title":"Linear Inversion of Band-Limited Reflection Seismograms","volume":"7","author":"Santosa","year":"1986","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3","article-title":"The lasso method for variable selection in the Cox model","volume":"16","author":"Tibshirani","year":"1997","journal-title":"Stat. Med."},{"key":"ref_16","unstructured":"Tikhonov, A.N., and Arsenin, V.I. (1977). Solutions of Ill-Posed Problems, Halsted Press."},{"key":"ref_17","unstructured":"Minsky, M.L., and Papert, S.A. (1987). Perceptrons\u2014Expanded Edition: An Introduction to Computational Geometry, MIT Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1002\/pmic.200400857","article-title":"Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents","volume":"4","author":"Prados","year":"2004","journal-title":"Proteomics"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Osuna, E., Freund, R., and Girosi, F. (1997). An improved training algorithm for support vector machines. Neural Networks for Signal Processing VII, Proceedings of the 1997 IEEE Signal Processing Society Workshop, Amelia Island, FL, USA, 24\u201326 September 1997, IEEE.","DOI":"10.1109\/NNSP.1997.622408"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.compbiomed.2018.12.017","article-title":"Clinical intelligence: New machine learning techniques for predicting clinical drug response","volume":"107","author":"Turki","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1002\/cmdc.201800533","article-title":"In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods","volume":"13","author":"Wang","year":"2018","journal-title":"ChemMedChem"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3389\/fchem.2018.00162","article-title":"Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category","volume":"6","author":"Yosipof","year":"2018","journal-title":"Front. Chem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-52937-5","article-title":"DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome","volume":"9","author":"Azarkhalili","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Turki, T., and Wei, Z. (2017). A link prediction approach to cancer drug sensitivity prediction. BMC Syst. Biol., 11.","DOI":"10.1186\/s12918-017-0463-8"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7381","DOI":"10.1109\/ACCESS.2017.2696523","article-title":"Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients","volume":"5","author":"Turki","year":"2017","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1840014","DOI":"10.1142\/S0219720018400140","article-title":"A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction","volume":"16","author":"Turki","year":"2018","journal-title":"J. Bioinform. Comput. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1182\/blood-2006-09-044974","article-title":"Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib","volume":"109","author":"Mulligan","year":"2007","journal-title":"Blood"},{"key":"ref_28","unstructured":"Bishop, C.M. (2009). Pattern Recognition and Machine Learning, Springer. Information science and statistics; Corrected at 8th printing 2009."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"658","DOI":"10.3389\/fonc.2019.00658","article-title":"New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers from Clinical Datasets","volume":"9","author":"Borisov","year":"2019","journal-title":"Front. Oncol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"256","DOI":"10.3389\/fgene.2019.00256","article-title":"A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer","volume":"10","author":"Tabl","year":"2019","journal-title":"Front. Genet."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/978-3-540-24674-9_27","article-title":"Gene Selection via Discretized Gene-Expression Profiles and Greedy Feature-Elimination","volume":"Volume 3025","author":"Vouros","year":"2004","journal-title":"Methods and Applications of Artificial Intelligence"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Allen, M. (2017). Data Trimming. The SAGE Encyclopedia of Communication Research Methods, SAGE Publications Inc.","DOI":"10.4135\/9781483381411"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Borisov, N., Tkachev, V., Muchnik, I., and Buzdin, A. (2017). Individual Drug Treatment Prediction in Oncology Based on Machine Learning Using Cell Culture Gene Expression Data, ACM Press.","DOI":"10.1145\/3155077.3155078"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/15384101.2017.1417706","article-title":"A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency","volume":"17","author":"Borisov","year":"2018","journal-title":"Cell Cycle"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/978-3-319-99492-5_9","article-title":"Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients","volume":"Volume 11100","author":"Rozonoer","year":"2018","journal-title":"Braverman Readings in Machine Learning. Key Ideas from Inception to Current State"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1177\/1087057104274091","article-title":"Development of CYP3A4 inhibition models: Comparisons of machine-learning techniques and molecular descriptors","volume":"10","author":"Arimoto","year":"2005","journal-title":"J. Biomol. Screen."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11710","DOI":"10.1039\/c1cp00051a","article-title":"Support vector machine regression (LS-SVM)\u2014An alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?","volume":"13","author":"Balabin","year":"2011","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1039\/c2an15972d","article-title":"Interpolation and extrapolation problems of multivariate regression in analytical chemistry: Benchmarking the robustness on near-infrared (NIR) spectroscopy data","volume":"137","author":"Balabin","year":"2012","journal-title":"Analyst"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4171","DOI":"10.1007\/s10661-012-2859-7","article-title":"Predicting copper concentrations in acid mine drainage: A comparative analysis of five machine learning techniques","volume":"185","author":"Betrie","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_40","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., M\u00fcller, A., Nothman, J., and Louppe, G. (2012). Scikit-learn: Machine Learning in Python. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1094\/PHYTO-03-17-0098-LE","article-title":"Statistical Power in Plant Pathology Research","volume":"108","author":"Gent","year":"2018","journal-title":"Phytopathology"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.jclinepi.2013.03.002","article-title":"Optimal type I and type II error pairs when the available sample size is fixed","volume":"66","author":"Ioannidis","year":"2013","journal-title":"J. Clin. Epidemiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1111\/j.1541-0420.2007.00782.x","article-title":"Type I and Type II Error Under Random-Effects Misspecification in Generalized Linear Mixed Models","volume":"63","author":"Alonso","year":"2007","journal-title":"Biometrics"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/bmsp.12132","article-title":"A note on Type S\/M errors in hypothesis testing","volume":"72","author":"Lu","year":"2019","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wetterslev, J., Jakobsen, J.C., and Gluud, C. (2017). Trial Sequential Analysis in systematic reviews with meta-analysis. BMC Med. Res. Methodol., 17.","DOI":"10.1186\/s12874-017-0315-7"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Borisov, N., Shabalina, I., Tkachev, V., Sorokin, M., Garazha, A., Pulin, A., Eremin, I.I., and Buzdin, A. (2019). Shambhala: A platform-agnostic data harmonizer for gene expression data. BMC Bioinf., 20.","DOI":"10.1186\/s12859-019-2641-8"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ghanem, R., Higdon, D., and Owhadi, H. (2015). Toward Machine Wald. Handbook of Uncertainty Quantification, Springer International Publishing.","DOI":"10.1007\/978-3-319-11259-6"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1137\/10080782X","article-title":"Optimal Uncertainty Quantification","volume":"55","author":"Owhadi","year":"2013","journal-title":"SIAM Rev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1051\/m2an\/2013083","article-title":"Optimal uncertainty quantification for legacy data observations of Lipschitz functions","volume":"47","author":"Sullivan","year":"2013","journal-title":"ESAIM Math. Model. Numer. Anal."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1001\/jama.2011.593","article-title":"A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer","volume":"305","author":"Hatzis","year":"2011","journal-title":"JAMA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s10549-013-2763-z","article-title":"Estrogen receptor (ER) mRNA expression and molecular subtype distribution in ER-negative\/progesterone receptor-positive breast cancers","volume":"143","author":"Itoh","year":"2014","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1158\/1078-0432.CCR-12-1359","article-title":"Biomarker analysis of neoadjuvant doxorubicin\/cyclophosphamide followed by ixabepilone or Paclitaxel in early-stage breast cancer","volume":"19","author":"Horak","year":"2013","journal-title":"Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.ccr.2012.08.007","article-title":"A small molecule inhibitor of ubiquitin-specific protease-7 induces apoptosis in multiple myeloma cells and overcomes bortezomib resistance","volume":"22","author":"Chauhan","year":"2012","journal-title":"Cancer Cell"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"9666","DOI":"10.18632\/oncotarget.5718","article-title":"The genetic and genomic background of multiple myeloma patients achieving complete response after induction therapy with bortezomib, thalidomide and dexamethasone (VTD)","volume":"7","author":"Terragna","year":"2016","journal-title":"Oncotarget"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1038\/leu.2014.140","article-title":"Gene expression profile alone is inadequate in predicting complete response in multiple myeloma","volume":"28","author":"Amin","year":"2014","journal-title":"Leukemia"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"D812","DOI":"10.1093\/nar\/gku1073","article-title":"The UCSC Cancer Genomics Browser: Update 2015","volume":"43","author":"Goldman","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.ccell.2015.01.003","article-title":"Recurrent DGCR8, DROSHA, and SIX Homeodomain Mutations in Favorable Histology Wilms Tumors","volume":"27","author":"Walz","year":"2015","journal-title":"Cancer Cell"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1002\/cncr.29871","article-title":"Biologic and clinical characteristics of adolescent and young adult cancers: Acute lymphoblastic leukemia, colorectal cancer, breast cancer, melanoma, and sarcoma: Biology of AYA Cancers","volume":"122","author":"Tricoli","year":"2016","journal-title":"Cancer"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s10549-009-0651-3","article-title":"Gene expression pathway analysis to predict response to neoadjuvant docetaxel and capecitabine for breast cancer","volume":"119","author":"Korde","year":"2010","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"R52","DOI":"10.1186\/bcr2611","article-title":"Changes in expression of oestrogen regulated and proliferation genes with neoadjuvant treatment highlight heterogeneity of clinical resistance to the aromatase inhibitor, letrozole","volume":"12","author":"Miller","year":"2010","journal-title":"Breast Cancer Res. BCR"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/tpj.2010.67","article-title":"Sequential changes in gene expression profiles in breast cancers during treatment with the aromatase inhibitor, letrozole","volume":"12","author":"Miller","year":"2012","journal-title":"Pharmacogenomics J."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"R5","DOI":"10.1186\/bcr2468","article-title":"Effect of training-sample size and classification difficulty on the accuracy of genomic predictors","volume":"12","author":"Popovici","year":"2010","journal-title":"Breast Cancer Res. BCR"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1093\/jnci\/djq524","article-title":"Gene pathways associated with prognosis and chemotherapy sensitivity in molecular subtypes of breast cancer","volume":"103","author":"Iwamoto","year":"2011","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/j.1349-7006.2012.02231.x","article-title":"GSTP1 expression predicts poor pathological complete response to neoadjuvant chemotherapy in ER-negative breast cancer","volume":"103","author":"Miyake","year":"2012","journal-title":"Cancer Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5832","DOI":"10.1073\/pnas.1201105109","article-title":"Seventeen-gene signature from enriched Her2\/Neu mammary tumor-initiating cells predicts clinical outcome for human HER2+:ER\u03b1- breast cancer","volume":"109","author":"Liu","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Shen, K., Qi, Y., Song, N., Tian, C., Rice, S.D., Gabrin, M.J., Brower, S.L., Symmans, W.F., O\u2019Shaughnessy, J.A., and Holmes, F.A. (2012). Cell line derived multi-gene predictor of pathologic response to neoadjuvant chemotherapy in breast cancer: A validation study on US Oncology 02-103 clinical trial. BMC Med. Genomics, 5.","DOI":"10.1186\/1755-8794-5-51"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1158\/1078-0432.CCR-06-2609","article-title":"Identification of molecular predictors of response in a study of tipifarnib treatment in relapsed and refractory acute myelogenous leukemia","volume":"13","author":"Raponi","year":"2007","journal-title":"Clin. Cancer Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1200\/JCO.2014.57.8963","article-title":"Accurate Prediction and Validation of Response to Endocrine Therapy in Breast Cancer","volume":"33","author":"Turnbull","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_69","first-page":"A68","article-title":"The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge","volume":"19","author":"Tomczak","year":"2015","journal-title":"Contemp. Oncol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"249","DOI":"10.5395\/rde.2015.40.3.249","article-title":"Statistical notes for clinical researchers: Type I and type II errors in statistical decision","volume":"40","author":"Kim","year":"2015","journal-title":"Restor. Dent. Endod."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"I377","DOI":"10.1161\/circ.102.suppl_1.I-377","article-title":"Guidelines based on fear of type II (false-negative) errors: Why we dropped the pulse check for lay rescuers","volume":"102","author":"Cummins","year":"2000","journal-title":"Circulation"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.aquatox.2010.09.020","article-title":"Evaluating the Type II error rate in a sediment toxicity classification using the Reference Condition Approach","volume":"101","author":"Rodriguez","year":"2011","journal-title":"Aquat. Toxicol."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/3\/713\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:20:33Z","timestamp":1760361633000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/3\/713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,22]]},"references-count":72,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["ijms21030713"],"URL":"https:\/\/doi.org\/10.3390\/ijms21030713","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,22]]}}}