Constructing an accurate predictive model for clinical decision-making on the basis of a relatively small number of tumor samples with high-dimensional microarray data remains a very challenging problem. The validity of such models has been seriously questioned due to their failure in clinical validation using independent samples. Besides the statistical issues such as selection bias, some studies further implied the probable reason was improper sample selection that did not resemble the genomic space defined by the training population. Assuming that predictions would be more reliable for interpolation than extrapolation, we set to investigate the impact of applicability domain (AD) on model performance in microarray-based genomic research by evaluating and comparing model performance for samples with different extrapolation degrees. We found that the issue of applicability domain may not exist in microarray-based genomic research for clinical applications. Therefore, it is not practicable to improve model validity based on applicability domain.
Citation: Shao L, Wu L, Fang H, Tong W, Fan X (2010) Does Applicability Domain Exist in Microarray-Based Genomic Research? PLoS ONE 5(6): e11055. https://doi.org/10.1371/journal.pone.0011055
Editor: Fabio Rapallo, University of East Piedmont, Italy
Received: April 14, 2010; Accepted: May 12, 2010; Published: June 10, 2010
Copyright: © 2010 Shao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the National Science Foundation of China (No. 30801556 and 30830121), Science Foundation of Chinese University (No. 2009QNA7031) and the Zhejiang Provincial Natural Science Foundation of China (No. R2080693). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Emerging technologies such as gene expression microarrays offer unprecedented opportunities for clinical cancer research , , . A decade of intensive research into developing predictive models that are capable of dividing patients into clinically relevant groups has yielded a number of demonstrable successes. Two primary examples of this are models to divide patients into groups with differing event-free survival , ,  and to identify groups of patients with different expected response to therapy , , .
However, challenges remain in this field , , . The validity of some models has been questioned due to their failure to clinical validate using independent samples. A recent example is a model for breast cancer prognosis built with two genes by that Reid et al.  that could not be validated by other investigators . From a statistical point of view, as reviewed by Simon , this type of prediction is a complicated problem and many factors, such as gene selection rules, sample resubstitution approaches, sample size concerns, and classification methods are involved. Fortunately, some of these factors have been extensively investigated and are incorporated as “best practices” in the research community. Ambroise, et al. demonstrated that the test/validation set must play no role in the gene selection process for unbiased prediction results to be obtained . Ransohoff, et al.  emphasized that over-fitting should be explicitly ruled out by reproducibility assessment early on, otherwise further research (that is, additional steps in the validation process) would be unwarranted and wasteful.
The importance of applicability domain (AD)  (i.e., the scope and limitations of a model) has long been discussed and emphasized in other research fields such as quantitative structure activity (property) relationship (QSAR) analysis , , , . AD in QSAR emphasizes that no matter how robust, significant and validated a model may be, it cannot be expected to reliably predict the modeled property for the entire universe of chemicals. Therefore, before a model is put into use for screening chemicals, its domain of application must be defined and predictions for only those chemicals that fall in this domain may be considered reliable .
However, the AD effect in genomic research has not been fully understood. The carcinoembryonic antigen (CEA) experience ,  from 40 years ago, where non-reproducible results were obtained largely due to the variation among the test sets in terms of the ‘spectrum’ of disease, initially implied the vital importance of selecting appropriate validation samples in order to reliably assess the reproducibility of statistical modeling results. Nevertheless, this issue has not yet been adequately addressed by the microarray-based ‘class prediction’ research community until now.
Two sources of divergence between training and validation samples exist: clinical differences such as diversity in cancer subtype, drug response, or prognosis, and genomic differences, or differences between gene expression patterns observed in the training and validation samples. We have undertaken a comprehensive investigation of the role of genomic differences in predictive model validation to determine if a genomic AD exists for microarray based ‘class-prediction’ modeling. We hypothesize that validation samples that more closely resemble the genomic space defined by the training set might be more likely to have accurate predictions than validation samples that significantly diverge from the genomic space defined by the training set.
A statistical measure called domain extrapolation  has been introduced to assess the genomic AD issue. Domain extrapolation is a measurement embedded in the model to place the patients in different groups according to their extrapolation degree. The role of genomic AD in microarray-based ‘class-prediction’ will be tested using three large-scale cancer datasets with six clinical endpoints  contributed to the MAQC Consortium and three prognostic datasets , , . To mimic the real world clinical situation, each dataset was divided into two sets, i.e., a training and validation set. We developed the domain extrapolation in the training set and followed with the assessment of its correlation with the model's predictive ability in the validation set. To the best of our knowledge this is the first attempt to systematically evaluate the issue of genomic AD in microarray-based genomic research.
Materials and Methods
Nine datasets, including three large-scale cancer datasets - breast cancer (BR) , multiple myeloma (MM)  and neuroblastoma (NB)  with six clinical endpoints contributed to the MAQC Consortium  and three datasets used in previously published prognostic modeling research , , , were selected and utilized in this study. A concise summary of the datasets is given in Table 1. More information about these datasets can be found from the main paper of MAQC phase II  and the original papers , , .
Briefly, each of the three large-scale cancer datasets has two endpoints, including the treatment response (BR-pCR and BR-erpos), the event-free survival (NB-EFS and MM-EFS) and the overall survival (NB-OS, MM-OS) which are related to cancer prognosis. The other three datasets are related to the survival of non-hodgkin lymphoma (NHL), breast cancer (BRC) and hepatocellular carcinoma (HCC). To simulate the real-world clinical application of genomic studies, two independent populations of patients for each dataset created by the MAQC Consortium or by the original researchers are retained in this study as the training and validation sets. The sample size for the training set varies between 33 and 340 and the ratio of positive events to negative events is from 0.18 to 1.60 while the validation sets range in size from 19 to 214.
Moreover, two positive (NB-PC, MM-PC) and negative (NB-NC, MM-NC) control endpoints available from the MAQC project were also included in this study, which are necessary to assess the performance of the clinically relevant endpoints against the theoretical maximum and minimum performance provided by the controls. The NB-PC and MM-PC were derived from the NB and MM datasets, respectively, with the endpoints denoted by the gender while the endpoints for the NB-NC and MM-NC were generated randomly.
Applicability domain (AD)
AD  of a microarray-based predictive model can be stated as the genomic or biological space, knowledge or information defined by the training set with which the predictive model has been developed, and thus for which it is applicable to new patients. Ideally, the model should only be used to make predictions within that domain by interpolation not outside that domain by extrapolation. In this study, we focus exclusively on genomic AD, or quantifying the degree of extrapolation or difference between the genomic space defined by the training set and each validation sample. The genomic AD of a model was defined based on the Euclidean distance  using the method shown as follows.
Suppose there is a training set (X) that contains n1 samples and p genes. We can define the mean value (mj) and standard deviation (sj) for each gene j (j = 1, 2,…,p) across the entire dataset as and , where xij is the expression value of gene j for individual xi (i = 1,2,…,n1). For any test set (Y) with n2 samples and p genes, let yij denote the expression value of the jth gene in ith (i = 1, 2,…, n2) sample. Then, the distance () beyond the training domain for the unknown sample yij for component j can be calculated by(1)
For each individual yi, di is greater than or equal to 0, with 0 indicating samples lying in domain. The larger di the more distantly away a sample removed an individual is from the training domain. For the sake of simplicity, the extrapolation degree di has been grouped into four categories: in domain (di = 0), less than 10% out of domain (di 0–10), 10–20% out of domain (di 10–20), and more than 20% out of domain (di>20).
As illustrated in the workflow shown in Figure 1, the analysis protocol starts on the left side of the graph by developing the best classifier based on the training set and ends on the right side by making a prediction about each individual in the validation set, where the predicted labels and corresponding extrapolation degrees are recorded in matrices L and D, respectively. To ensure statistical validity, the procedure was repeated 500 times, resulting in 500 different classifiers from the training sets and 500 predictions for each individual in the validation sets. Detailed information about model construction procedures is provided in Figure S1. In this study, nearest-centroid (NC) , k-nearest neighbor (kNN)  and support vector machines (SVM)  were used as classification algorithms.
Based on the 500-run results, we further divided the predictions in matrix L into subsets according to the category of extrapolation degrees (i.e., “in domain”, “<10% out of domain”, “10–20% out of domain”, and “>20% out of domain”) deposited in D. The prediction performance (as measured by Matthews correlation coefficient (MCC)) for samples in each subset provides an illustration of model performance versus the stepwise increase of extrapolation degree. The Matthews Correlation Coefficient (MCC) is defined as:(3)
Where is the number of true positives, is the number of true negatives, is the number of false positives and is the number of false negatives. MCC varies between −1 and +1 with 0 corresponding to random prediction.
The prediction MCC as a function of extrapolation degree category for the nine datasets using kNN is shown in Figure 2, using NC in Figure S2, and using SVM in Figure S3. In each of the graphs, the red section of the pie-charts representing the data points show the proportion of the total testing set contained in that category of extrapolation degree. Generally, no significant impact on AD is observed, as evidenced by the slight increase in MCC for samples lying out of domain compared to those in domain for most datasets except BR-erpos. In BR-erpos validation set, fewer than 2% of the samples were in each of the 10–20% extrapolation and >20% extrapolation. We re-analyzed the results by distributing samples into the training and validation sets so that each of these categories has around 10% of the samples in the validation set. This modification resulted in the disappearance of any significant effect of extrapolation degree on MCC for each of the classification algorithms (Figure 3).
The proportion of red in each pie chart represents the proportion of total validation set samples contained in that extrapolation degree category. Here ‘0’, ‘10’, ‘20’ and ‘>20’ in the X-axis mean ‘In domain’, ‘0–10% out of domain’, ‘10–20% out of domain’ and ‘more than 20% out of domain’, respectively.
Three classification algorithms including NC, kNN and SVM are used, and the percentage of samples in each interval out of domain is adjusted to more than 10%. The proportion of red in each pie chart represents the proportion of total validation set samples contained in that extrapolation degree category. Here ‘0’, ‘10’, ‘20’ and ‘>20’ in the X-axis mean ‘In domain’, ‘0–10% out of domain’, ‘10–20% out of domain’ and ‘more than 20% out of domain’, respectively.
In order to accurately assess the upper and lower bounds of performance and provide a point of reference for the prognostic datasets, two positive control datasets (i.e., NB-PC and MM-PC) and two negative control datasets (i.e., NB-NC and MM-NC) were also investigated. Figure 4 demonstrates the results for these datasets for each of the three different classification methods used. The decrease in model performance is nearly negligible for MM-PC, while model performance drastically deteriorated for NB-PC when samples lay more than 20% degree out of domain. Considering that more than 95% of the samples lie in the domain for NB-PC, the same strategy utilized above was also used to ensure a larger percentage of samples in each interval, which yielded significantly smoothed curves shown in Figure 3. Additionally, negligible variation of model performance is observed for negative control datasets, where NB-NC and MM-NC (Figure 4) supports these conclusions.
Three classification algorithms including NC, kNN and SVM are used. The proportion of red in each pie chart represents the proportion of total validation set samples contained in that extrapolation degree category. Here ‘0’, ‘10’, ‘20’ and ‘>20’ in the X-axis mean ‘In domain’, ‘0–10% out of domain’, ‘10–20% out of domain’ and ‘more than 20% out of domain’, respectively.
Although differences in genome-wide gene expression patterns has been suggested previously as a possible reason for some failed applications of microarray based ‘class-prediction’ models to validate clinical models , , this is the first comprehensive investigation to identify whether genomic AD is truly a concern for microarray-based predictive modeling. Our results strongly suggest that genomic AD may not exist for clinical microarray-based genomic research. In other words, the expectation of improving model validity based on genomic AD is not practical in microarray-based genomic research.
The exact reasons for the negligible impact of genomic AD on model performance is beyond the scope of this study. However, two aspects may provide some explanation to this phenomenon: first, the genomic AD created by the training set may contain much more variability than is represented by the signature genes selected in the predictive models; second, the domain definition method utilized in this study might not be sensitive enough to capture the difference between samples inside and outside the domain. In clinical applications, model AD should be defined in not only a statistical or genomic but also a biological way, representing the training domain defined by parameters selected in statistical models and a priori clinical information. In other words, the insignificant impact of a genomic AD for complex endpoints does not negate the importance of considering clinical parameters when predicting independent validation samples. A simple but important example is that the information of cancer subtype must be considered before model development and use to ensure the reliability of any prediction, since the prognosis may differ significantly between subtypes .
As an interesting side note to this study, the three well known classification methods, i.e. kNN, NC and SVM, used in this study (with corresponding results provided in Figure 2 and Figures S2 and S3, respectively) gave very similar prediction performance for samples with different extrapolation degrees. This offers further evidence for the lack of significant differences among a large number of classification methods reported for microarray applications in terms of the predictive performance, a conclusion also proposed by the newly-finished community-wide study, MAQC-II .
In conclusion, our study found that the applicability domain may not exist for microarray based clinical genomic research, and that predictive model performance did not depend on a measurement of distance between a validation sample and the training set used to create the model. Because of this, a strategy of considering applicability domain to increase model validation performance is unlikely to be successful. However, the negative conclusion in this study does not deny the importance of considering a priori clinical information associated with prognosis such as cancer subtype and estrogen receptor status for breast cancer patients before making an individual prediction, the importance of which has already been proposed by other studies.
Detailed model construction procedures. The construction of the best classifier is shown as follows (see the superscripts in this figure): 1. Stratified random sample splitting - We use the 70/30 splitting, where the 70% samples are for classifier construction, and the resulting classifier is then used to predict the 30% samples to obtain the prediction performance of the classifier. To ensure statistical validity, we repeat this procedure 500 times, resulting in 500 different classifiers. 2. Filtering - This step is to generate an initial pool of probesets for further analysis. Specifically, the original pool of probesets is firstly sorted by the absolute signal-to-noise (SN) ratio, and then the 200 top ranked probesets are retained for further analysis. 3. Feature selection - We apply a sequential selection method, with the best performed probeset being sequentially added into the model to develop a classifier, which is then evaluated on the 30% samples. The process is repeated by incrementally adding one probeset at a time to generate more classifiers. 4. Classifier selection - For classifier i (i corresponds to the number of probesets selected in the classifier), if the performance MCC for following five consecutive classifiers is smaller than or equal to that of classifier i, the process is stopped and classifier i is selected as the best classifier. Otherwise, Steps 3 and 4 are repeated. 5. Prediction - Base on the best classifier, the predicted labels and corresponding extrapolation degrees for samples in the validation set are calculated and recorded. Steps 1 to 5 is repeated 500 times, generating two matrices L(500×p) and D(500×p), which deposit the predicted labels and corresponding extrapolation degrees, respectively. Here, p indicates the number of samples in the validation set.
(0.29 MB TIF)
Prediction MCC as a function of extrapolation degree for nine datasets using NC classifier. The proportion of red in each pie chart represents the proportion of total validation set samples contained in that extrapolation degree category. Here ‘0’, ‘10’, ‘20’ and ‘>20’ in the X-axis mean ‘In domain’, ‘0–10% out of domain’, ‘10–20% out of domain’ and ‘more than 20% out of domain’, respectively.
(0.52 MB TIF)
Prediction MCC as a function of extrapolation degree for nine datasets using SVM classifier. The proportion of red in each pie chart represents the proportion of total validation set samples contained in that extrapolation degree category. Here ‘0’, ‘10’, ‘20’ and ‘>20’ in the X-axis mean ‘In domain’, ‘0–10% out of domain’, ‘10–20% out of domain’ and ‘more than 20%% out of domain’, respectively.
(0.50 MB TIF)
The authors would like to thank the MAQC-II data 3providers and Dr. Leming Shi (NCTR/FDA) for their great help with the data, and thank Dr. Reagan Kelly (NCTR/FDA) for the helpful discussions. The views presented in this article do not necessarily reflect those of the U.S. Food and Drug Administration.
Conceived and designed the experiments: LS HF WT XF. Analyzed the data: LS LW XF. Wrote the paper: LS XF.
- 1. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21: 33–37.PO BrownD. Botstein1999Exploring the new world of the genome with DNA microarrays.Nat Genet213337
- 2. DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, et al. (1996) Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 14: 457–460.J. DeRisiL. PenlandPO BrownML BittnerPS Meltzer1996Use of a cDNA microarray to analyse gene expression patterns in human cancer.Nat Genet14457460
- 3. Fan XH, Shi LM, Fang H, Cheng YY, Perkins R, et al. (2010) DNA microarrays are predictive of cancer prognosis: a re-evaluation. Clin Cancer Res 16: 629–636.XH FanLM ShiH. FangYY ChengR. Perkins2010DNA microarrays are predictive of cancer prognosis: a re-evaluation.Clin Cancer Res16629636
- 4. van't Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536.LJ van't VeerHY DaiMJ van de VijverYDD HeAAM Hart2002Gene expression profiling predicts clinical outcome of breast cancer.Nature415530536
- 5. van de Vijver MJ, He YD, van 't Veer LJ, Dai H, Hart AAM, et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999–2009.MJ van de VijverYD HeLJ van 't VeerH. DaiAAM Hart2002A gene-expression signature as a predictor of survival in breast cancer.N Engl J Med34719992009
- 6. Mulligan AM, Pinnaduwage D, Bull SB, O'Malley FP, Andrulis IL (2008) Prognostic effect of basal-like breast cancers is time dependent: Evidence from tissue microarray studies on a lymph node-negative cohort. Clin Cancer Res 14: 4168–4174.AM MulliganD. PinnaduwageSB BullFP O'MalleyIL Andrulis2008Prognostic effect of basal-like breast cancers is time dependent: Evidence from tissue microarray studies on a lymph node-negative cohort.Clin Cancer Res1441684174
- 7. Ayers M, Symmans WF, Stec J, Damokosh AI, Clark E, et al. (2004) Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 22: 2284–2293.M. AyersWF SymmansJ. StecAI DamokoshE. Clark2004Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer.J Clin Oncol2222842293
- 8. Mariadason JM, Arango D, Shi QH, Wilson AJ, Corner GA, et al. (2003) Gene expression profiling-based prediction of response of colon carcinoma cells to 5-fluorouracil and camptothecin. Cancer Res 63: 8791–8812.JM MariadasonD. ArangoQH ShiAJ WilsonGA Corner2003Gene expression profiling-based prediction of response of colon carcinoma cells to 5-fluorouracil and camptothecin.Cancer Res6387918812
- 9. Iwao-Koizumi K, Matoba R, Ueno N, Kim SJ, Ando A, et al. (2005) Prediction of docetaxel response in human breast cancer by gene expression profiling. J Clin Oncol 23: 422–431.K. Iwao-KoizumiR. MatobaN. UenoSJ KimA. Ando2005Prediction of docetaxel response in human breast cancer by gene expression profiling.J Clin Oncol23422431
- 10. Merseburger AS, Kuczyk MA, Serth J, Bokemeyer C, Young DY, et al. (2003) Limitations of tissue microarrays in the evaluation of focal alterations of bcl-2 and p53 in whole mount derived prostate tissues. Oncol Rep 10: 223–228.AS MerseburgerMA KuczykJ. SerthC. BokemeyerDY Young2003Limitations of tissue microarrays in the evaluation of focal alterations of bcl-2 and p53 in whole mount derived prostate tissues.Oncol Rep10223228
- 11. Reid JF, Lusa L, De Cecco L, Coradini D, Veneroni S, et al. (2005) Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J Natl Cancer Inst 97: 927–930.JF ReidL. LusaL. De CeccoD. CoradiniS. Veneroni2005Limits of predictive models using microarray data for breast cancer clinical treatment outcome.J Natl Cancer Inst97927930
- 12. Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365: 488–492.S. MichielsS. KoscielnyC. Hill2005Prediction of cancer outcome with microarrays: a multiple random validation strategy.Lancet365488492
- 13. Ma XJ, Wang ZC, Ryan PD, Isakoff SJ, Barmettler A, et al. (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5: 607–616.XJ MaZC WangPD RyanSJ IsakoffA. Barmettler2004A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen.Cancer Cell5607616
- 14. Simon R, Radmacher MD, Dobbin K, McShane LM (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95: 14–18.R. SimonMD RadmacherK. DobbinLM McShane2003Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification.J Natl Cancer Inst951418
- 15. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A 99: 6562–6566.C. AmbroiseGJ McLachlan2002Selection bias in gene extraction on the basis of microarray gene-expression data.Proc Natl Acad Sci U S A9965626566
- 16. Ransohoff DF (2004) Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer 4: 309–314.DF Ransohoff2004Rules of evidence for cancer molecular-marker discovery and validation.Nat Rev Cancer4309314
- 17. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22: 69–77.A. TropshaP. GramaticaVK Gombar2003The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models.QSAR Comb Sci226977
- 18. Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, et al. (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111: 1361–1375.L. ErikssonJ. JaworskaAP WorthMTD CroninRM McDowell2003Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.Environ Health Perspect11113611375
- 19. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MTD, et al. (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships - The report and recommendations of ECVAM Workshop 52. Atla-Alternatives to Laboratory Animals 33: 155–173.TI NetzevaAP WorthT. AldenbergR. BenigniMTD Cronin2005Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships - The report and recommendations of ECVAM Workshop 52.Atla-Alternatives to Laboratory Animals33155173
- 20. Kolossov E, Stanforth R (2007) The quality of QSAR models: problems and solutions. SAR QSAR Environ Res 18: 89–100.E. KolossovR. Stanforth2007The quality of QSAR models: problems and solutions.SAR QSAR Environ Res1889100
- 21. Ransohoff DF, Feinstein AR (1978) Problems of spectrum and bias in evaluating efficacy of diagnostic test. N Engl J Med 299: 926–930.DF RansohoffAR Feinstein1978Problems of spectrum and bias in evaluating efficacy of diagnostic test.N Engl J Med299926930
- 22. Sackett DL (1987) Zlinkoff honor lecture -basic research, clinical research, clinical epidemiology, and general internal-medicine. J Gen Intern Med 2: 40–47.DL Sackett1987Zlinkoff honor lecture -basic research, clinical research, clinical epidemiology, and general internal-medicine.J Gen Intern Med24047
- 23. Tong WD, Xie Q, Hong HX, Shi LM, Fang H, Perkins R (2004) Assessment of Prediction Confidence and Domain Extrapolation of Two Structure-Activity Relationship Models for Predicting Estrogen Receptor Binding Activity. Environ Health Perspect 112: 1249–1254.WD TongQ. XieHX HongLM ShiH. FangR. Perkins2004Assessment of Prediction Confidence and Domain Extrapolation of Two Structure-Activity Relationship Models for Predicting Estrogen Receptor Binding Activity.Environ Health Perspect11212491254
- 24. MAQC Consortium (2009) The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models. Submitted. MAQC Consortium2009The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models.Submitted
- 25. Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, et al. (2003) Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. Lancet 361: 923–929.N. IizukaM. OkaH. Yamada-OkabeM. NishidaY. Maeda2003Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection.Lancet361923929
- 26. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, et al. (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 346: 1937–1947.A. RosenwaldG. WrightWC ChanJM ConnorsE. Campo2002The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.N Engl J Med34619371947
- 27. Hess KR, Anderson K, Symmans WF, Valero V, Ibrahim N, et al. (2006) Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol 24: 4236–4244.KR HessK. AndersonWF SymmansV. ValeroN. Ibrahim2006Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.J Clin Oncol2442364244
- 28. Shaughnessy JD, Zhan FH, Burington BE, Huang YS, Colla S, et al. (2007) A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 109: 2276–2284.JD ShaughnessyFH ZhanBE BuringtonYS HuangS. Colla2007A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1.Blood10922762284
- 29. Oberthuer A, Berthold F, Warnat P, Hero B, Kahlert Y, et al. (2006) Customized oligonucleotide microarray gene expression based classification of neuroblastoma patients outperforms current clinical risk stratification. J Clin Oncol 24: 5070–5078.A. OberthuerF. BertholdP. WarnatB. HeroY. Kahlert2006Customized oligonucleotide microarray gene expression based classification of neuroblastoma patients outperforms current clinical risk stratification.J Clin Oncol2450705078
- 30. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MTD, et al. (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships - The report and recommendations of ECVAM Workshop 52. Altern Lab Anim 33: 155–173.TI NetzevaAP WorthT. AldenbergR. BenigniMTD Cronin2005Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships - The report and recommendations of ECVAM Workshop 52.Altern Lab Anim33155173
- 31. Theodoridis S, Koutroumbas K (2006) Pattern Recognition. San Diego, USA: Elsevier. S. TheodoridisK. Koutroumbas2006Pattern Recognition.San Diego, USAElsevier
- 32. Mukhopadhyay A, Maulik U (2009) Towards improving fuzzy clustering using support vector machine: Application to gene expression data. Pattern Recognit 42: 2744–2763.A. MukhopadhyayU. Maulik2009Towards improving fuzzy clustering using support vector machine: Application to gene expression data.Pattern Recognit4227442763
- 33. Matthews BW (1975) Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405: 442–451.BW Matthews1975Comparison of predicted and observed secondary structure of T4 phage lysozyme.Biochim Biophys Acta405442451
- 34. Nguyen PL, Taghian AG, Katz MS, Niemierko A, Raad RFA, et al. (2008) Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy. J Clin Oncol 26: 2373–2378.PL NguyenAG TaghianMS KatzA. NiemierkoRFA Raad2008Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy.J Clin Oncol2623732378
- 35. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97: 77–87.S. DudoitJ. FridlyandTP Speed2002Comparison of discrimination methods for the classification of tumors using gene expression data.J Am Stat Assoc977787