[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.
A schematic portraying expression profiling of a sample vs a reference by spotted microarrays using probe-switching (dye swap) experiments. The results yield replicate expression levels of the ratios of the complementary DNAs (cDNAs) in the sample vs the reference. mRNA indicates messenger RNA.

A schematic portraying expression profiling of a sample vs a reference by spotted microarrays using probe-switching (dye swap) experiments. The results yield replicate expression levels of the ratios of the complementary DNAs (cDNAs) in the sample vs the reference. mRNA indicates messenger RNA.

Figure 2.
A schematic depicting the behavior of noise (false-positive data or artifacts). Genomewide profiling of a sample vs a reference generates a data set including tens of thousands of ratios. The gene list includes a small fraction of differentially expressed genes (true-positive genes, < 5%) and a predominant majority of genes that are not differentially expressed (true-negative genes, > 95%). Because of noise, the true-negative genes appear as if they were differentially expressed. Furthermore, the distribution of noise differs between data sets. The heterogeneous colors of the large squares depict the idea that individual data sets have unique noise distributions that are dependent on experimental variations and on the quality of each data set. For example, large ratios may be false in a poor-quality data set and small ratios may be true in a better-quality data set. Highly specific discovery is applied to individual data sets. It discovers the small number of differentially expressed genes by filtering the dominant noise generated by the large number of the genes that are not differentially expressed.

A schematic depicting the behavior of noise (false-positive data or artifacts). Genomewide profiling of a sample vs a reference generates a data set including tens of thousands of ratios. The gene list includes a small fraction of differentially expressed genes (true-positive genes, < 5%) and a predominant majority of genes that are not differentially expressed (true-negative genes, > 95%). Because of noise, the true-negative genes appear as if they were differentially expressed. Furthermore, the distribution of noise differs between data sets. The heterogeneous colors of the large squares depict the idea that individual data sets have unique noise distributions that are dependent on experimental variations and on the quality of each data set. For example, large ratios may be false in a poor-quality data set and small ratios may be true in a better-quality data set.28 Highly specific discovery is applied to individual data sets. It discovers the small number of differentially expressed genes by filtering the dominant noise generated by the large number of the genes that are not differentially expressed.

Table. 
Percentage of False-Positive Ratios in a Data Set Containing 500 and 19500 True and False States of Genetic Expression, Respectively*
Percentage of False-Positive Ratios in a Data Set Containing 500 and 19500 True and False States of Genetic Expression, Respectively*
1.
Alizadeh  AAEisen  MBDavis  E  et al.  Distinct types of diffuse late B-cell lymphomas identified by gene expression profiling.  Nature 2000;403503- 511PubMedCrossref
2.
Bittner  MMeltzer  PChen  C  et al.  Molecular classification of cutaneous melanoma by gene expression profiling.  Nature 2000;406536- 540PubMedCrossref
3.
Ramaswamy  SRoss  KNLander  ESGolub  TR A molecular signature of metastasis in primary solid tumors.  Nat Genet 2003;3349- 54PubMedCrossref
4.
Dudoit  SGentleman  RCQuackenbush  J Open source software for the analysis of microarray data.  Biotechniques 2003;(suppl)45- 51PubMed
5.
Chen  YDougherty  ERBittner  ML Ratio-based decisions and the quantitative analysis of cDNA microarray images.  J Biomed Opt 1997;2364- 374Crossref
6.
Nishizuka  SChen  STGwadry  FG  et al.  Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling.  Cancer Res 2003;635243- 5250PubMed
7.
Schena  MShalon  DDavis  RWBrown  PO Quantitative monitoring of gene expression patterns with a complementary DNA microarray.  Science 1995;270467- 470PubMedCrossref
8.
Lockhart  DJDong  HByrne  MC  et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays.  Nat Biotechnol 1996;141675- 1680PubMedCrossref
9.
DeRisi  JLIyer  VRBrown  PO Exploring the metabolic and genetic control of gene expression on a genomic scale.  Science 1997;278680- 686PubMedCrossref
10.
Fathallah-Shaykh  HRigen  MZhao  L-J  et al.  Mathematical modeling of noise and discovery of genetic expression classes in gliomas.  Oncogene 2002;217164- 7174PubMedCrossref
11.
van 't Veer  LJDai  Hvan de Vijver  MJ  et al.  Gene expression profiling predicts clinical outcome of breast cancer.  Nature 2002;415530- 536PubMedCrossref
12.
Perou  CMJeffrey  SSRees  CA  et al.  Distinctive gene expression patterns in human mammary epithelial cells and breast cancers.  Proc Natl Acad Sci U S A 1999;969212- 9217PubMedCrossref
13.
Pomeroy  SLTamayo  PSturla  LM  et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression.  Nature 2002;415436- 442PubMedCrossref
14.
Golub  TRSlonim  DKTamayo  P  et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.  Science 1999;286531- 537PubMedCrossref
15.
Sorlie  TPerou  CMTibshirani  R  et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.  Proc Natl Acad Sci U S A 2001;9810869- 10874PubMedCrossref
16.
Fathallah-Shaykh  HMHe  BZhao  L-J  et al.  Genomic expression discovery predicts pathways and opposing functions behind phenotypes.  J Biol Chem 2003;27823830- 23833PubMedCrossref
17.
Marton  MJDeRisi  JLIyer  VR  et al.  Drug target validation and identification of secondary drug target effects using DNA microarrays.  Nat Med 1998;41293- 1301PubMedCrossref
18.
Hood  L Systems biology: integrating technology, biology, and computation.  Mech Ageing Dev 2003;1249- 16PubMedCrossref
19.
Holstege  FCJennings  EGWyrick  JJ  et al.  Dissecting the regulatory circuitry of a eukaryotic genome.  Cell 1998;95717- 728PubMedCrossref
20.
Ideker  TThorsson  VRanish  JA  et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.  Science 2001;292929- 934PubMedCrossref
21.
Fathallah-Shaykh  HM Logical networks inferred from highly specific discovery of transcriptionally regulated genes predict protein states in cultured gliomas.  Biochem Biophys Res Commun 2005;3361278- 1284Crossref
22.
Fathallah-Shaykh  HM Genomic discovery reveals a molecular system for resistance to ER and oxidative stress in cultured glioma.  Arch Neurol 2005;62233- 236Crossref
23.
Kothapalli  RYoder  SJMane  SLoughran  TP  Jr Microarray results: how accurate are they?  BMC Bioinformatics 2002;322PubMedCrossref
24.
Ntzani  EEIoannidis  JP Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment.  Lancet 2003;3621439- 1444PubMedCrossref
25.
Tan  PKDowney  TJSpitznagel  EL  Jr  et al.  Evaluation of gene expression measurements from commercial microarray platforms.  Nucleic Acids Res 2003;315676- 5684PubMedCrossref
26.
Michiels  SKoscielny  SHill  C Prediction of cancer outcome with microarrays: a multiple random validation strategy.  Lancet 2005;365488- 492Crossref
27.
Nielsen  TOHsu  FDO'Connell  JX  et al.  Tissue microarray validation of epidermal growth factor receptor and SALL2 in synovial sarcoma with comparison to tumors of similar histology.  Am J Pathol 2003;1631449- 1456PubMedCrossref
28.
Fathallah-Shaykh  HHe  BZhao  L-JBadruddin  A A mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.  Nucleic Acids Res 2004;323807- 3814PubMedCrossref
29.
Gambetti  PParchi  PChen  SG Hereditary Creutzfeldt-Jakob disease and fatal familial insomnia.  Clin Lab Med 2003;2343- 64PubMedCrossref
30.
Hood  L Leroy Hood expounds the principles, practice and future of systems biology.  Drug Discov Today 2003;8436- 438PubMedCrossref
31.
Ideker  TGalitski  THood  L A new approach to decoding life: systems biology.  Annu Rev Genomics Hum Genet 2001;2343- 372PubMedCrossref
32.
Li  JLHayden  MRAlmqvist  EW  et al.  A genome scan for modifiers of age at onset in Huntington disease: the HD MAPS study.  Am J Hum Genet 2003;73682- 687PubMedCrossref
33.
Roberts  TG  JrLynch  TJ  JrChabner  BA The phase III trial in the era of targeted therapy: unraveling the “go or no go” decision.  J Clin Oncol 2003;213683- 3695PubMedCrossref
Neurological Review
November 2005

MicroarraysApplications and Pitfalls

Author Affiliations

Author Affiliations: Department of Neurological Sciences, Section of Neuro-oncology, Rush University Medical Center, Chicago, Ill.

 

DAVID E.PLEASUREMD

Arch Neurol. 2005;62(11):1669-1672. doi:10.1001/archneur.62.11.1669
Abstract

Microarrays are simple assays that measure the relative expression levels of tens of thousands of genes. Excitement about their importance and potential contributions to biology and medicine has been intense. Nonetheless, recent insights into the limitations and pitfalls of microarrays have led to caution about data interpretation. Microarrays are very useful but they are also very misleading; better data analysis tools are needed to improve accuracy.

Over the past half century, scientists have studied cause-and-effect relationships between known genes and biological phenotypes or human disease. Recent technological advances have changed the landscape of biomedical research. The complete genomes of several organisms are now available, and the expression of tens of thousands of genes may be assayed by microarrays. Genomes are rich sources of complex genetic information, most of which is unknown and unpredictable. Hence, the term discovery has been introduced to imply finding without preconceived bias which genes are relevant to a biological phenotype and how the genes interact.

In a single assay, microarrays generate tens of thousands of measurements of the relative levels of messenger RNA expression. When first developed, microarrays appeared to hold great promise for translating genomics into significant advances in basic biology and medicine. The National Institutes of Health (Bethesda, Md), universities, and drug companies have invested heavily in various applications of microarrays. Nonetheless, recent findings have uncovered major pitfalls that cast doubt on the interpretation of microarray data. Herein, I review the technology of complementary DNA (cDNA) microarrays, their applications and pitfalls, and future directions in data analysis.

The experimental system

Spotted arrays may include tens of thousands of cDNAs laid on glass slides. Each experiment uses 2 RNA samples and measures the relative expression level of the cDNAs in 1 messenger RNA as compared with the other (Figure 1). The messenger RNAs are reverse transcribed to cDNAs and labeled with fluorescent dyes, mixed, and hybridized to the glass slide. After washing, the spot-bound fluorescent dyes are excited by lasers of appropriate wavelengths to generate 2 “scanned” images, which correspond to the samples. Images are analyzed to quantify (1) the signal within each spot and (2) a small rim of background surrounding each spot. The principal measurement is the expression ratio of each spot: Image description not available.

A log2(ratio) greater than 0 implies up-regulation and a log2(ratio) less than 0 implies down-regulation. A data set of a single experiment contains tens of thousands of ratios.

Microarray experiments using spotted arrays are usually designed to compare each of several samples to a single reference RNA that is common to all experiments. The data are expressed in a matrix whose columns correspond to samples and rows to genes; each column represents a distinct experiment. Analytical strategies often apply multivariate statistics including clustering, the self-organizing maps of Kohonen (neural networks), and principal component analysis or multidimensional scaling.16

Potential applications of microarrays in biology and medicine

The enthusiasm about the potential of microarrays has been intense.79 Experimental designs are usually aimed at discovering (1) patterns of expression that classify disease phenotypes and predict clinical behavior or (2) molecular targets and systems that create the biology. The first goal is based on the intuitive idea that genome-scale molecular expression refines the pathological classification of disease. Specifically, classifications based on molecular expression are expected to be more accurate and sensitive than those based on microscopy. Preliminary proofs of principles include reports of patterns of genetic expression that predict new classifications of central nervous system embryonal tumors, gliomas, large B-cell lymphoma, and breast carcinoma.1,1015 For example, the molecular classes may either replicate the pathological distinction or divide the subjects within the same pathological class into subgroups that predict distinct clinical behaviors like long-term vs short-term survival times and drug response vs resistance.

The idea that the global transcriptional response constitutes molecular phenotypes has recently received attention.12,16,17 In this model, phenotypes are created by molecular systems in which single genes or molecules belong to rich networks of dynamic molecular interactions that include transcriptional regulation, signaling pathways, protein-protein, and protein–nucleic acid interactions.16,18 Examples of microarray applications in systems biology include the discovery of (1) the regulation of the transcriptional response when yeast cells encounter nutrients, (2) the yeast galactose-utilization pathway, and (3) the principles of balanced genetic expression and opposing molecular functions behind the phenotypes of meningiomas and cultured gliomas.16,1922 Theoretically, one could apply microarrays to discover new molecular classifications of neurological diseases, to study and define the molecular systems that create each individual phenotype, and to perturb the network to find the best targets that transition the whole system between phenotypes.

Pitfalls of microarrays

Following the initial hype and excitement about microarrays, their pitfalls and limitations are causing a hard reality check. Current methods for microarray expression data analysis require numerous samples and yield measurements of low specificity. Kothapalli et al23 examined microarray data from 2 different systems. They report inconsistencies in sequence fidelity of the spotted microarrays, variability of differential expression, low specificity of cDNA probes, discrepancy in fold-change calculations, and lack of probe specificity for different isoforms of a gene. Ntzani and Ioannidis24 examined 84 large-scale microarray expression data sets that address major clinical outcomes including death, metastasis, recurrence, and response to therapy. They found that these studies show variable prognostic performance. Tan et al25 examined gene expression measurements generated from identical RNA preparations that were obtained using 3 commerically available microarray platforms from Affymetrix, Amersham, and Agilent. Correlations in gene expression levels and comparisons for significant gene expression changes in this subset showed considerable divergence across the different platforms. Michiels et al26 reanalyzed data from the 7 largest published studies that have attempted to predict prognosis of patients with cancer on the basis of DNA microarray analysis. The results reveal that the list of genes identified as predictors of prognosis was highly unstable and molecular signatures were strongly dependent on the selection of patients in the training sets. In addition, 5 of the 7 studies did not classify patients better than chance. The poor specificity and reproducibility are not surprising considering all the experimental variables that affect the quality of the data sets. These include variations in the laboratories, individuals, probe labeling, biochemical reactions, scanners, and lasers. Because of the low specificity, validation by other methods for measuring gene expression has become the “gold standard.”25,27 However, biological samples are not always abundant, and the price tag of validating all the genes discovered by microarray expression profiling is astronomical.

The nature of the problem

The specificity of the discovery should be stringent when the data sets consist of tens of thousands of genes and contain a predominant majority of noise. To illustrate, let us consider the example of a data set containing 500 true states of genetic expression (up-regulated or down-regulated) and 19 500 false-positive states (Table). Specificities of 99% and 95% yield 195 and 975 false-positive expression states, respectively. Thus, an analytical method having 100% sensitivity and 99% specificity discovers 695 genes (500 + 195), 28% (195/695) of which are false positive. Another method having 50% sensitivity and 99% specificity yields 445 genes (250 + 195), 44% (195/445) of which are false positive. This example illustrates the limitations of statistical significance when noise is predominant.

Microarrays assay for the relative expression levels of a cDNA (1) in a biological sample as compared with another and (2) relative to other cDNAs within the same sample. The accuracy of fold changes is critical for data analysis. The results of Kothapalli et al23 reveal poor reproducibility and discrepancies of fold-change calculations between microarrays (interarray). Furthermore, the accuracy of calculations of fold changes of genes within a single microarray (intra-array) is not known. Low specificity, the preponderance and heterogeneity of noise, and inaccurate fold-change calculations impose significant limitations on data analysis. For example, apparent molecular classifications may be caused by data set–specific noise and the results of 1 laboratory may disintegrate when tested independently.24 Furthermore, variations in gene expression levels between biological samples may be caused by noise and not biological heterogeneity.

Highly specific expression discovery

Recent reports describe mathematical models that shed light on the behavior of noise in microarray data sets and algorithms that discover highly specific states of genetic expression (up-regulated or down-regulated) from genomewide expression profiling.10,28 The mathematical models incorporate the principles of (1) preponderance and (2) heterogeneity of noise. The preponderance of noise implies that (1) the overwhelming majority of the genes on the array are not differentially expressed between samples (true negatives) and (2) the truly negative genes generate false-positive expression data (noise). Noise heterogeneity implies that the distribution of noise varies between data sets depending on quality. These principles may be summarized as follows:

  • Each sample vs reference comparison generates tens of thousands of expression ratios.

  • The model is based on the idea that less than 5% of all the genomic genes are truly differentially expressed between the sample and reference (true positives). The expression levels of the other more than 95% are not expected to be different (true negatives).

  • Even when the expression levels of the genes do not differ between the sample and reference, the predominant majority of their measured expression ratios are not equal to 1 (noise, artifacts, or false positives).

  • The distributions of the false positives vary widely between experiments; the variability is determined by quality.

  • True-positive (<5%) and false-positive ratios (>95%) share the same distributions.

The mathematical tools generate highly specific discovery by modeling and filtering noise (Figure 2). The use of mathematical modeling and filters is common; to name a few examples, engineers apply filters to solve problems of noise in cellular telephones, digital music, and digital television.

Significance and future directions

Highly specific genome-scale discovery of states of genetic expression has applications in all aspects of biology and medicine; it facilitates hypothesis-driven research and sets the stage for studies in systems biology.10,16,28 Several models that explain the relationship of genotype to phenotype have evolved over the past 40 years. First is the model of a single genetic lesion causing a phenotype; an example is sickle cell disease. A second model is that of several genotypes causing the same phenotype; examples include malignant brain tumors and Alzheimer disease. A third model is that of a single genetic lesion causing distinct phenotypes depending on polymorphisms; examples include hereditary Creutzfeldt-Jakob disease and fatal familial insomnia.29 Data from the highly specific genome-scale discovery in meningiomas are consistent with a fourth model of complex molecular systems.16,18,30,31 In this model, single genes or molecules of the cell belong to rich networks of molecular interactions that include transcriptional regulation, signaling pathways, protein-protein, and protein–nucleic acid interactions.16 These 4 models are not exclusive; for instance, complex molecular systems may also explain the heterogeneity of the clinical phenotypes of a dominant genetic lesion like expansion of the CAG repeats of Huntington disease.32

The idea that molecular systems, and not single genes, create phenotypes has important biological and therapeutic implications. The majority of clinical trials that have targeted single or a few genes have failed; most compounds that show efficacy in preclinical experiments and phase 1 and phase 2 clinical trials turn out to be ineffective in very expensive phase 3 trials. Hopefully, systems biology will improve the decision making for the transition to phase 3 clinical trials.33 The results of the meningioma study support the idea that the phenotypes are created by the principles of (1) multiplicity and (2) balancing of opposing molecular functions. Multiplicity is apparent because of the multifunctionality of single genes and because a given phenotype is caused not by a single molecule but rather by up-regulating several genes that promote a desirable “aberrant” function and by down-regulating a number of genes that prevent it. Thus, a “normal” biological phenotype seems to be created, maintained, and controlled by a tight balancing of opposing molecular functions. Meningiomas disturb this balanced expression to promote their phenotypes.16 The principle of multiplicity of complex molecular systems may explain the shortcomings of drug development. Targeting single genes or single pathways is likely to fail because molecular systems have redundant molecules or pathways that bypass the blockade. It is intuitive that targets selected based on molecular systems are more likely to be clinically effective than targets selected based on single molecules or pathways.

Microarrays can be extremely useful for many biological fields, particularly clinical neurology and systems biology, but they can also be very misleading. Not unlike many fields in physics, the full potential of microarrays awaits advances in mathematics. We ought to step back to the drawing board to develop better tools for data analysis.

Back to top
Article Information

Correspondence: Hassan M. Fathallah-Shaykh, MD, Department of Neurological Sciences, Section of Neuro-oncology, Rush University Medical Center, Chicago, IL 60612 (hfathall@rush.edu).

Accepted for Publication: July 29, 2004.

References
1.
Alizadeh  AAEisen  MBDavis  E  et al.  Distinct types of diffuse late B-cell lymphomas identified by gene expression profiling.  Nature 2000;403503- 511PubMedCrossref
2.
Bittner  MMeltzer  PChen  C  et al.  Molecular classification of cutaneous melanoma by gene expression profiling.  Nature 2000;406536- 540PubMedCrossref
3.
Ramaswamy  SRoss  KNLander  ESGolub  TR A molecular signature of metastasis in primary solid tumors.  Nat Genet 2003;3349- 54PubMedCrossref
4.
Dudoit  SGentleman  RCQuackenbush  J Open source software for the analysis of microarray data.  Biotechniques 2003;(suppl)45- 51PubMed
5.
Chen  YDougherty  ERBittner  ML Ratio-based decisions and the quantitative analysis of cDNA microarray images.  J Biomed Opt 1997;2364- 374Crossref
6.
Nishizuka  SChen  STGwadry  FG  et al.  Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling.  Cancer Res 2003;635243- 5250PubMed
7.
Schena  MShalon  DDavis  RWBrown  PO Quantitative monitoring of gene expression patterns with a complementary DNA microarray.  Science 1995;270467- 470PubMedCrossref
8.
Lockhart  DJDong  HByrne  MC  et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays.  Nat Biotechnol 1996;141675- 1680PubMedCrossref
9.
DeRisi  JLIyer  VRBrown  PO Exploring the metabolic and genetic control of gene expression on a genomic scale.  Science 1997;278680- 686PubMedCrossref
10.
Fathallah-Shaykh  HRigen  MZhao  L-J  et al.  Mathematical modeling of noise and discovery of genetic expression classes in gliomas.  Oncogene 2002;217164- 7174PubMedCrossref
11.
van 't Veer  LJDai  Hvan de Vijver  MJ  et al.  Gene expression profiling predicts clinical outcome of breast cancer.  Nature 2002;415530- 536PubMedCrossref
12.
Perou  CMJeffrey  SSRees  CA  et al.  Distinctive gene expression patterns in human mammary epithelial cells and breast cancers.  Proc Natl Acad Sci U S A 1999;969212- 9217PubMedCrossref
13.
Pomeroy  SLTamayo  PSturla  LM  et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression.  Nature 2002;415436- 442PubMedCrossref
14.
Golub  TRSlonim  DKTamayo  P  et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.  Science 1999;286531- 537PubMedCrossref
15.
Sorlie  TPerou  CMTibshirani  R  et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.  Proc Natl Acad Sci U S A 2001;9810869- 10874PubMedCrossref
16.
Fathallah-Shaykh  HMHe  BZhao  L-J  et al.  Genomic expression discovery predicts pathways and opposing functions behind phenotypes.  J Biol Chem 2003;27823830- 23833PubMedCrossref
17.
Marton  MJDeRisi  JLIyer  VR  et al.  Drug target validation and identification of secondary drug target effects using DNA microarrays.  Nat Med 1998;41293- 1301PubMedCrossref
18.
Hood  L Systems biology: integrating technology, biology, and computation.  Mech Ageing Dev 2003;1249- 16PubMedCrossref
19.
Holstege  FCJennings  EGWyrick  JJ  et al.  Dissecting the regulatory circuitry of a eukaryotic genome.  Cell 1998;95717- 728PubMedCrossref
20.
Ideker  TThorsson  VRanish  JA  et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.  Science 2001;292929- 934PubMedCrossref
21.
Fathallah-Shaykh  HM Logical networks inferred from highly specific discovery of transcriptionally regulated genes predict protein states in cultured gliomas.  Biochem Biophys Res Commun 2005;3361278- 1284Crossref
22.
Fathallah-Shaykh  HM Genomic discovery reveals a molecular system for resistance to ER and oxidative stress in cultured glioma.  Arch Neurol 2005;62233- 236Crossref
23.
Kothapalli  RYoder  SJMane  SLoughran  TP  Jr Microarray results: how accurate are they?  BMC Bioinformatics 2002;322PubMedCrossref
24.
Ntzani  EEIoannidis  JP Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment.  Lancet 2003;3621439- 1444PubMedCrossref
25.
Tan  PKDowney  TJSpitznagel  EL  Jr  et al.  Evaluation of gene expression measurements from commercial microarray platforms.  Nucleic Acids Res 2003;315676- 5684PubMedCrossref
26.
Michiels  SKoscielny  SHill  C Prediction of cancer outcome with microarrays: a multiple random validation strategy.  Lancet 2005;365488- 492Crossref
27.
Nielsen  TOHsu  FDO'Connell  JX  et al.  Tissue microarray validation of epidermal growth factor receptor and SALL2 in synovial sarcoma with comparison to tumors of similar histology.  Am J Pathol 2003;1631449- 1456PubMedCrossref
28.
Fathallah-Shaykh  HHe  BZhao  L-JBadruddin  A A mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.  Nucleic Acids Res 2004;323807- 3814PubMedCrossref
29.
Gambetti  PParchi  PChen  SG Hereditary Creutzfeldt-Jakob disease and fatal familial insomnia.  Clin Lab Med 2003;2343- 64PubMedCrossref
30.
Hood  L Leroy Hood expounds the principles, practice and future of systems biology.  Drug Discov Today 2003;8436- 438PubMedCrossref
31.
Ideker  TGalitski  THood  L A new approach to decoding life: systems biology.  Annu Rev Genomics Hum Genet 2001;2343- 372PubMedCrossref
32.
Li  JLHayden  MRAlmqvist  EW  et al.  A genome scan for modifiers of age at onset in Huntington disease: the HD MAPS study.  Am J Hum Genet 2003;73682- 687PubMedCrossref
33.
Roberts  TG  JrLynch  TJ  JrChabner  BA The phase III trial in the era of targeted therapy: unraveling the “go or no go” decision.  J Clin Oncol 2003;213683- 3695PubMedCrossref
×