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PubMed1984

Echocardiographic assessment of the subaortic root after implantation of high or low profile mitral prostheses.

In order to evaluate the influence of the profile of mitral prostheses on the outflow tract of the left ventricle, 99 patients underwent M-Mode echocardiography before and after surgery. In 67 cases high profile mitral prostheses had been implanted (48 mechanical and 19 biological); in 32 cases low profile mitral prostheses had been implanted (22 mechanical, 10 biological). Echocardiography showed that the implantation of high profile mitral prostheses did not induce changes of left ventricular diastolic and systolic dimensions ( EDDLV and ESDLV ) while induced significant reduction of left ventricular outflow tract (LVOT) dimension and LVOT/ EDDLV index with subsequent changes in transaortic flow. On the other hand, low profile prostheses did not induce significant changes of left ventricular dimensions, LVOT and LVOT/ EDDLV index. Finally, low profile biological mitral valve prostheses ( Liotta type) induced a significant increase of post-operative dimension of LVOT and LVOT/ EDDLV index. Authors conclude: that the implantation of high profile mitral prostheses changes LVOT dimension and transaortic flow and, that M-Mode echocardiography is a reliable technique to point out these changes.

Adult↗
PubMed1980

Biochemical individuality and the recognition of personal profiles with a computer.

Multitest analysis of an individual's blood provides a biochemical profile that reflects his identity and pathophysiological state. During a six-week period we repeatedly profiled 10 volunteers for 22 different analytes, using continuous-flow and discrete analyzers (SMAC, KA 150 enzyme analyzer, ABA-100, AutoAnalyzers) and manual procedures. Two years later, we obtained multiple follow-up profiles. Using linear discriminant functions derived from the first five (or first 10) specimens from each subject, we were able correctly to identify 96% (or 100%) of the specimens collected during the remainder of the six-week testing period. Ninety percent of the two-year follow-up specimens were correctly identified when we used all the original profiles to calculate the discriminant functions. Deliberately mislabeled specimens were also correctly identified by discriminant analysis. Profiles of individual samples (and average profiles for each subject) were graphically displayed as computer-drawn faces and non-linear maps. Covariances between pairs of tests on repeated profiles differed significantly for different subjects. Inter-test relationships were graphically displayed by nonlinear mapping.

Autoanalysis↗
PubMedDate not supplied

Serotypes and DNA fingerprint profiles of Pasteurella multocida isolated from raptors.

Pasteurella multocida isolates from 21 raptors were examined by DNA fingerprint profile and serotyping methods. Isolates were obtained from noncaptive birds of prey found in 11 states from November 28, 1979, through February 10, 1993. Nine isolates were from bald eagles, and the remaining isolates were from hawks, falcons, and owls. Seven isolates were members of capsule group A, and 14 were nonencapsulated. One isolate was identified as somatic type 3, and another was type 3,4,7; both had unique HhaI DNA fingerprint profiles. Nineteen isolates expressed somatic type 1 antigen; HhaI profiles of all type 1 isolates were identical to each other and to the HhaI profile of the reference somatic type 1, strain X-73. The 19 type 1 isolates were differentiated by sequential digestion of DNA with HpaII; four HpaII fingerprint profiles were obtained. The HpaII profile of one isolate was identical to the HpaII profile of strain X-73. Incidence of P. multocida somatic type 1 in raptors suggests that this type may be prevalent in other wildlife or wildlife environments.

Animals↗
PubMed1996

Inherited susceptibility determines the distribution of dense low-density lipoprotein subfraction profiles in familial combined hyperlipidemia.

Familial combined hyperlipidemia (FCH) is a heritable lipid disorder, in which dense low-density lipoprotein (LDL) subfraction profiles due to a predominance of small dense LDL particles are frequently observed. These small dense LDL particles are associated with cardiovascular disease. Using segregation analysis, we investigated to what extent these LDL subfraction profiles are genetically determined; also, the mode of inheritance was studied. Individual LDL subfraction profiles were determined by density gradient ultracentrifugation in 623 individuals of 40 well-defined Dutch FCH families. The individual LDL subfraction profile was defined as a quantitative trait by the continuous variable K, a reliable estimate of the relative contribution of each LDL subfraction to the overall profile. Variation in parameter K due to age, sex, and hormonal status was taken into account by introducing liability classes. Segregation analysis was performed by fitting a series of class D regressive models, implemented in the Statistical Analysis for Genetic Epidemiology (SAGE) program, after which genetic models were compared using log-likelihood ratio tests. Our data show that 60% of the variability of parameter K could be explained by lipid and lipoprotein levels and that a major autosomal locus, recessively inherited, with a population frequency of .42 +/- .07, and an additional polygenic component of .25 best explained the clustering of atherogenic dense LDL subfraction profiles in these FCH families. Therefore, dense LDL subfraction profiles, associated with elevated lipid levels, appear to have a genetic basis in FCH.

Adolescent↗
PubMed1998

Blood lipid profiles in children with acute lymphoblastic leukemia.

BACKGROUND: Abnormal blood lipid profiles have been associated with cancer. The objective of this study was to investigate the frequency and clinical significance of altered lipid profiles in children with acute lymphoblastic leukemia (ALL), the most common form of malignant disease in this age group. METHODS: Fasting blood lipid profiles (cholesterol [C], triglycerides [TG], high density lipoprotein [HDL], low density lipoprotein, very low density lipoprotein, apolipoproteins A1 [apo A1] and B, and lipoprotein a [Lp(a)]) were obtained in 24 children with ALL at diagnosis, 16 children during consolidation therapy with L-asparaginase, and 18 children during maintenance therapy without L-asparaginase. For comparison the authors studied lipid profiles in 15 children previously treated for leukemia, 15 healthy control children, and 17 children with other forms of cancer, both localized and widespread. RESULTS: An altered blood lipid profile was observed at the time of diagnosis of ALL. Statistically significant values included elevated TG (1.82+/-1.23 mmol/L), reduced HDL-C (0.54+/-0.24 mmol/L), and reduced ApoA1 (0.77+/-0.18 g/L) levels. A wide range of Lp(a) levels (0-1990 mg/L) were observed. Significantly reduced HDL-C (0.55+/-0.20 mmol/L) and ApoA1 (0.69+/-0.22 g/L) were observed in children with widespread but not localized solid tumors at diagnosis. C and TG correlated with serum albumin levels. Significant therapy-related changes in lipid profiles were observed in children with ALL during combination therapy with L-asparaginase (extremely elevated TG levels [3.34+/-2.82 mmol/L] and a striking reduction in Lp(a) levels) that were not observed during combination therapy without L-asparaginase or in children during treatment for solid tumors. In this small study there was no relation between these abnormalities and either thromboembolic events or pancreatitis. Blood lipid profiles in children with ALL returned to normal on completion of therapy. CONCLUSIONS: The lipid abnormalities observed at diagnosis in children with widespread cancer (ALL or solid tumors) may reflect altered nutritional states or altered lipid metabolism. Reduced concentrations of Lp(a) and elevated TG levels suggest L-asparaginase specific alterations and may provide insight into the toxicity associated with this drug.

Antineoplastic Agents↗
PubMedDate not supplied

Plasmid profile analysis and antibiotic resistance of Salmonella strains from clinical isolates in Cluj-Napoca.

Resistance patterns, plasmid profiles and the genetic resistance determinants were investigated in 38 isolates of Salmonella enterica serotype Typhimurium and 19 isolates of Salmonella enterica serovar Enteritidis derived from children hospitalized in two clinics in Cluj-Napoca, during the period of 1995-1997. Incidence of plasmid and antibiotic resistance was very high in Salmonella typhimurium isolates. All strains were resistant to almost all antibiotics tested but susceptible to the third generation cephalosporines and fluoroquinolones. We identified three resistance patterns and six plasmid profiles. Each plasmid profile was characterized by the presence of two large plasmids of 150-180 Kbp. Approximately 60% of strains harbored three or four small plasmids of 1.3 to 9.5 Kbp. The plasmids of 8.5 Kbp encoded resistance to beta-lactam antibiotics and were non-conjugative. The other small plasmids were cryptic and also non-conjugative. Salmonella enteritidis isolates were susceptible to many antibiotics, except Tetracycline and Trimethoprim-Sulfamethoxazole. We identified three different resistance patterns but nine plasmid profiles. All plasmid profiles were characterized by the presence of a large plasmid (> 100 Kbp). The number and the diversity of small plasmids were higher than in S. typhimurium strains. There was no parallelism between resistance and plasmid profile: for the same resistance pattern a number of two or three plasmid profiles were found. Our conclusions are that Salmonella typhimurium strains were multiresistant to antibiotics and that many genetically different strains of Salmonella typhimurium and Salmonella enteritidis were responsible for gastroenteritis in children from Cluj County. The increasing antibiotic resistance highlights the need for more refined methods in genetic and epidemiological characterization of bacteria involved in gastrointestinal infections.

Anti-Bacterial Agents↗
PubMed2026

Forensic applicability of genetic profile generation from hair roots and shafts: Integration of retrotransposon polymorphisms and morphological predictors.

Genetic profiles were successfully obtained from hair samples both directly plucked from the scalp and indirectly from personal items such as combs and hairbrushes. Additionally, 100 genetic profiles were generated from buccal swabs from all donors, allowing the calculation of population allele and genotype frequencies. Complete genetic profiles were recovered from samples containing less than 0.012 ng of total nuclear DNA. Nuclear DNA yield per hair root was highly variable, whereas hair shafts yielded up to 2 ng of total nuDNA and in some cases less than 0.1 ng. Multiple correspondence analysis (MCA) revealed that hair growth phase and the presence of a root were not significantly associated with successful profile recovery; instead, greater hair thickness and direct sampling correlated with higher success rates. In certain cases, the Insertion/Null (INNUL) markers system, InnoTyper 21, outperformed the Power Plex Fusion 6 C STR kit. For forensic purposes, using the entire hair shaft provided better profiling outcomes than using the root alone. All Insertion/Null (INNUL) markers were in Hardy-Weinberg equilibrium, except for a few loci showing minor linkage disequilibrium. These results highlight the analytical potential of INNUL markers for obtaining nuclear DNA profiles from hair, even in challenging forensic contexts.

Humans↗
PubMed2006

Signature combinatorial splicing profiles of rat cardiac- and smooth-muscle Cav1.2 channels with distinct biophysical properties.

l-type (Ca(v)1.2) voltage-gated calcium channels play an essential role in muscle contraction in the cardiovascular system. Alternative splicing of the pore-forming Ca(v)1.2 subunit provides potent means to enrich the functional diversity of the channels. There are 11 alternatively spliced exons identified in rat Ca(v)1.2 gene and random rearrangements may generate up to hundreds of combinatorial splicing profiles. Due to such complexity, the real combinatorial splicing profiles of Ca(v)1.2 have not been solved. This study investigated whether the 11 alternatively spliced exons are spliced randomly or linked and if linked, how many combinatorial splicing profiles can be arranged in cardiac- and smooth-muscle cells. By examining three full-length cDNA libraries of the Ca(v)1.2 transcripts isolated from rat heart and aorta, our results showed that the arrangements of some of the alternatively spliced exons are tissue-specific and tightly linked, giving rise to only 41 alternative combinatorial profiles, of which 29 have not been reported. Interestingly, the 41 combinatorial profiles were distinctively distributed in the three Ca(v)1.2 libraries and the one named "heart 1-50" contained unexpected splice variants. Significantly, the tissue-specific cardiac- and smooth-muscle combinatorial splicing profiles of Ca(v)1.2 channels demonstrated distinct electrophysiological properties that may help rationalize the differences observed in native currents. The unique sequences in these tissue-specific splice variants may provide the potential targets for drug design and screening.

Alternative Splicing↗
PubMed2001

Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles.

Molecular classification of tumors based on their gene expression profiles promises to significantly refine diagnosis and management of cancer patients. The establishment of organ-specific gene expression patterns represents a crucial first step in the clinical application of the molecular approach. Here, we report on the gene expression profiles of 154 primary adenocarcinomas of the lung, colon, and ovary. Using high-density oligonucleotide arrays with 7129 gene probe sets, comprehensive gene expression profiles of 57 lung, 51 colon, and 46 ovary adenocarcinomas were generated and subjected to principle component analysis and to a cross-validated prediction analysis using nearest neighbor classification. These statistical analyses resulted in the classification of 152 of 154 of the adenocarcinomas in an organ-specific manner and identified genes expressed in a putative tissue-specific manner for each tumor type. Furthermore, two tumors were identified, one in the colon group and another in the ovarian group, that did not conform to their respective organ-specific cohorts. Investigation of these outlier tumors by immunohistochemical profiling revealed the ovarian tumor was consistent with a metastatic adenocarcinoma of colonic origin and the colonic tumor was a pleomorphic mesenchymal tumor, probably a leiomyosarcoma, rather than an epithelial tumor. Our results demonstrate the ability of gene expression profiles to classify tumors and suggest that determination of organ-specific gene expression profiles will play a significant role in a wide variety of clinical settings, including molecular diagnosis and classification.

Adenocarcinoma↗
PubMed2003

Partitioning large-sample microarray-based gene expression profiles using principal components analysis.

Principal components analysis (PCA) is useful for reproducing the total variation among hundreds or thousands of continuously-scaled variables with a much smaller number of unobservable variables called 'latent factors'. The CLUSFAVOR computer program was used to implement PCA for identifying groups of genes with similar expression profiles from a large number of genes used on DNA microarrays. This paper describes the principal components solution to the factor model of the correlation matrix R, calculation of eigenvalues and eigenvectors of R, extraction of factors, and calculation of factor loadings and identification of genes with similar loading patterns to construct groups of genes with similar expression profiles. With regard to extraction of factors, it was found that more than 90% of the total variance in input data could be accounted for by extracting factors whose eigenvalues exceed unity. Bipolar factors containing strong positive and negative loadings can also be used for identifying two unique groups of genes, since expression profiles of genes that load positive are unlike expression profiles of genes that load negative on the same factor. While PCA does not provide the absolute answer to a multidimensional problem, it nevertheless can provide a heuristic with which natural groupings of genes with similar expression profiles can be assembled. While cluster analysis essentially generates a single dendogram (tree branch) containing every gene in the input data, PCA can be used to assemble gene expression profiles that strongly correlate with the latent factors accounting for a majority of total variance. Example results for CLUSFAVOR computer program runs are provided.

Gene Expression Profiling↗
PubMed2000

The multidimensional pain inventory profiles in patients with chronic cancer-related pain: an examination of generalizability.

This study examined the generalizability of the non-malignant pain patient profiles based on the Multidimensional Pain Inventory (MPI) to patients with cancer-related pain. Data were collected from 112 cancer patients. In total, 107/112 patients completed the MPI. Of the 96% of patients classified, only 60% were classified by the three main profiles. In this sample, there were 47.7% (n=51) Adaptive Copers, 9.3% (n=10) Dysfunctional, 2.8% (n=3) Interpersonally Distressed; 32.7% (n=35) Anomalous; 3.8% (n=4) Hybrid; and 3.8% (n=4) Unanalyzable. Because of the significantly lower pain severity, interference and affective distress scores, the Anomalous group could be considered Highly Adaptive. Given that 80% were classified as either Adaptive or Anomalous, these findings suggest that while the MPI-based profiles do apply, a two profile classification system may be more suitable for cancer patients than the usual three. In particular, the low proportion of patients classified as Interpersonally Distressed may reflect important differences in social support for cancer patients compared with non-cancer patients. Whereas the MPI-based profiles are consistent across non-malignant pain problems, it appears that the nature of cancer may affect the MPI-based profile classification system more than non-malignant pain problems do.

Adaptation, Psychological↗
PubMed2004

Differential gene-expression profiles associated with gastric adenoma.

Gastric adenomas may eventually progress to adenocarcinomas at varying rates. The purpose of the present study was to identify gene-expression profiles linked to the heterogeneous nature of gastric adenoma as compared to adenocarcinoma. Suppression subtractive hybridisation analysis was performed to extract relevant genes from two cases of low- and high-grade gastric adenomas. The identified genes were quantified by RT-PCR in 14 low-grade adenoma, nine high-grade adenoma and nine adenocarcinoma samples, followed by hierarchical clustering analysis to separate tumours into groups according to their gene-expression profiles. Nine genes previously implicated in carcinogenesis in a variety of organs, including three genes related to gastric adenocarcinoma, were identified. The overexpression of these genes in gastric adenoma has not been reported previously. The clustering analysis of these nine genes across 32 cases identified three groups, one of which consisted primarily of adenocarcinomas, whereas the other two groups consisted of adenomas. One group of adenomas, characterised by larger tumour size, exhibited gene-expression profiles of an intestinal cell lineage implicated in the pathogenesis of an intestinal-type gastric adenocarcinoma. Another adenoma group consisting of low-grade adenomas with smaller tumour size exhibited a unique expression profile. In conclusion, clustering analysis of expression profiles using a limited number of genes may serve as molecular markers for gastric adenoma with different biological properties. Although the prognostic values of these gene-expression profiles need to be evaluated in further follow-up study of adenoma cases, these findings add new insights to (a) our understanding of the pathogenesis of gastric tumours, (b) the development of specific tumour markers for clinical practice, and (c) the design of novel therapeutic targets.

Adenocarcinoma↗
PubMed2006

Gene expression signatures and biomarkers of noninvasive and invasive breast cancer cells: comprehensive profiles by representational difference analysis, microarrays and proteomics.

We have characterized comprehensive transcript and proteomic profiles of cell lines corresponding to normal breast (MCF10A), noninvasive breast cancer (MCF7) and invasive breast cancer (MDA-MB-231). The transcript profiles were first analysed by a modified protocol for representational difference analysis (RDA) of cDNAs between MCF7 and MDA-MB-231 cells. The majority of genes identified by RDA showed nearly complete concordance with microarray results, and also led to the identification of some differentially expressed genes such as lysyl oxidase, copper transporter ATP7A, EphB6, RUNX2 and a variant of RUNX2. The altered transcripts identified by microarray analysis were involved in cell-cell or cell-matrix interaction, Rho signaling, calcium homeostasis and copper-binding/sensitive activities. A set of nine genes that included GPCR11, cadherin 11, annexin A1, vimentin, lactate dehydrogenase B (upregulated in MDA-MB-231) and GREB1, S100A8, amyloid beta precursor protein, claudin 3 and cadherin 1 (downregulated in MDA-MB-231) were sufficient to distinguish MDA-MB-231 from MCF7 cells. The downregulation of a set of transcripts for proteins involved in cell-cell interaction indicated these transcripts as potential markers for invasiveness that can be detected by methylation-specific PCR. The proteomic profiles indicated altered abundance of fewer proteins as compared to transcript profiles. Antisense knockdown of selected transcripts led to inhibition of cell proliferation that was accompanied by altered proteomic profiles. The proteomic profiles of antisense transfectants suggest the involvement of peptidyl-prolyl isomerase, Raf kinase inhibitor and 80 kDa protein kinase C substrate in mediating the inhibition of cell proliferation.

Biomarkers, Tumor↗
PubMed2005

Influence of in vivo growth on human glioma cell line gene expression: convergent profiles under orthotopic conditions.

Defining the molecules that regulate tumor cell survival is an essential prerequisite for the development of targeted approaches to cancer treatment. Whereas many studies aimed at identifying such targets use human tumor cells grown in vitro or as s.c. xenografts, it is unclear whether such experimental models replicate the phenotype of the in situ tumor cell. To begin addressing this issue, we have used microarray analysis to define the gene expression profile of two human glioma cell lines (U251 and U87) when grown in vitro and in vivo as s.c. or as intracerebral (i.c.) xenografts. For each cell line, the gene expression profile generated from tissue culture was significantly different from that generated from the s.c. tumor, which was significantly different from those grown i.c. The disparity between the i.c gene expression profiles and those generated from s.c. xenografts suggests that whereas an in vivo growth environment modulates gene expression, orthotopic growth conditions induce a different set of modifications. In this study the U251 and U87 gene expression profiles generated under the three growth conditions were also compared. As expected, the profiles of the two glioma cell lines were significantly different when grown as monolayer cultures. However, the glioma cell lines had similar gene expression profiles when grown i.c. These results suggest that tumor cell gene expression, and thus phenotype, as defined in vitro is affected not only by in vivo growth but also by orthotopic growth, which may have implications regarding the identification of relevant targets for cancer therapy.

Animals↗
PubMedDate not supplied

Gene expression profiles in rat liver slices exposed to hepatocarcinogenic enzyme inducers, peroxisome proliferators, and 17alpha-ethinylestradiol.

Transcription profiling is used as an in vivo method for predicting the mode-of-action class of nongenotoxic carcinogens. To set up a reliable in vitro short-term test system DNA microarray technology was combined with rat liver slices. Seven compounds known to act as tumor promoters were selected, which included the enzyme inducers phenobarbital, alpha-hexachlorocyclohexane, and cyproterone acetate; the peroxisome proliferators WY-14,643, dehydroepiandrosterone, and ciprofibrate; and the hormone 17alpha-ethinylestradiol. Rat liver slices were exposed to various concentrations of the compounds for 24 h. Toxicology-focused TOXaminer DNA microarrays containing approximately 1500 genes were used for generating gene expression profiles for each of the test compound. Hierarchical cluster analysis revealed that (i) gene expression profiles generated in rat liver slices in vitro were specific allowing classification of compounds with similar mode of action and (ii) expression profiles of rat liver slices exposed in vitro correlate with those induced after in vivo treatment (reported previously). Enzyme inducers and peroxisome proliferators formed two separate clusters, confirming that they act through different mechanisms. Expression profiles of the hormone 17alpha-ethinylestradiol were not similar to any of the other compounds. In conclusion, gene expression profiles induced by compounds that act via similar mechanisms showed common effects on transcription upon treatment in vivo and in rat liver slices in vitro.

Androgen Antagonists↗
PubMed2004

DBRF-MEGN method: an algorithm for deducing minimum equivalent gene networks from large-scale gene expression profiles of gene deletion mutants.

MOTIVATION: Large-scale gene expression profiles measured in gene deletion mutants are invaluable sources for identifying gene regulatory networks. Signed directed graph (SDG) is the most common representation of gene networks in genetics and cell biology. However, no practical procedure that deduces SDGs consistent with such profiles has been developed. RESULTS: We developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method in which an algorithm deduces the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. Positive (or negative) directed edges representing positive (or negative) gene regulations are deduced by comparing the gene expression level between the wild-type and mutant. The most parsimonious SDGs are deduced using graph theoretical procedures. Compensation for excess removal of edges by restoring a minimum number of edges makes the method applicable to cyclic gene networks. Use of independent groups of edges greatly reduces the computational cost, thus making the method applicable to large-scale expression profiles. We confirmed the applicability of our method by applying it to the gene expression profiles of 265 Saccharomyces cerevisiae deletion mutants, and we confirmed our method's validity by comparing the pheromone response pathway, general amino acid control system, and copper and iron homeostasis system deduced by our method with those reported in the literature. Interpretation of the gene network deduced from the S. cerevisiae expression profiles by using our method led to the prediction of 132 transcriptional targets and modulators of transcriptional activity of 18 transcriptional regulators. AVAILABILITY: The software is available on request.

Algorithms↗
PubMed2006

TSSub: eukaryotic protein subcellular localization by extracting features from profiles.

UNLABELLED: This paper introduces a new subcellular localization system (TSSub) for eukaryotic proteins. This system extracts features from both profiles and amino acid sequences. Four different features are extracted from profiles by four probabilistic neural network (PNN) classifiers, respectively (the amino acid composition from whole profiles; the amino acid composition from the N-terminus of profiles; the dipeptide composition from whole profiles and the amino acid composition from fragments of profiles). In addition, a support vector machine (SVM) classifier is added to implement the residue-couple feature extracted from amino acid sequences. The results from the five classifiers are fused by an additional SVM classifier. The overall accuracies of this TSSub reach 93.0 and 77.4% on Reinhardt and Hubbard's eukaryotic protein dataset and Huang and Li's eukaryotic protein dataset, respectively. The comparison with existing methods results shows TSSub provides better prediction performance than existing methods. AVAILABILITY: The web server is available from http://166.111.24.5/webtools/TSSub/index.html.

Algorithms↗
PubMed2004

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments.

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides' areas characterized by an abnormal concentration of low/high differential expression values, which we define as 'patterns of differentials'. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile's quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis.

Algorithms↗