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Results for “unsupervised clustering”

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PubMed2004

Clustering of gene hypermethylation associated with clinical risk groups in neuroblastoma.

BACKGROUND: Neuroblastoma is the most common extracranial solid malignancy in infancy and childhood, but the biological factors involved in its development and progression are still unclear. Transcriptional silencing of tumor suppressor genes mediated by hypermethylation of promoter CpG islands is a hallmark of human tumors. We addressed the clinical relevance of promoter hypermethylation in neuroblastoma. METHODS: We examined the methylation status of 45 candidate genes representative of many cellular pathways in 10 neuroblastoma cell lines and of 10 of these genes in 145 tumor samples (118 of them were primary neuroblastomas). We used Fisher's exact test to examine the association of CpG island methylation and clinical subgroups and Kaplan-Meier analysis to determine the association between methylation and survival in primary tumors. Cluster analysis was used to group cell lines and tumors by gene methylation status. Bonferroni-corrected statistical tests were two-sided. RESULTS: Clustering of neuroblastoma cell lines on the basis of hypermethylation distinguished lines with MYCN amplification (a negative prognostic factor) from those without it (P =.012). Promoter hypermethylation of the developmental gene HOXA9 was associated with mortality in noninfant patients (P =.04) and in tumors lacking MYCN amplification (P =.023). Hypermethylation of the proapoptotic gene TMS1 and the cell cycle gene CCND2 was associated with stage 4-progressing tumors (P<.001), but the genes were never methylated in stage 4S tumors, which undergo spontaneous regression. Hypermethylation of the differentiation gene RARbeta2 was associated with patient survival (P =.032). Unsupervised hierarchical cluster analysis of all tumors based on methylation of the 10 genes separated several clinically relevant groups of tumors. CONCLUSIONS: Profiling the status of CpG island hypermethylation in human primary neuroblastomas may have clinicopathologic value.

Cell Line, Tumor↗
PubMed2005

CLUE: cluster-based retrieval of images by unsupervised learning.

In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.

Algorithms↗
PubMed2001

Pattern recognition in flow cytometry.

BACKGROUND: Analytical flow cytometry (AFC), by quantifying sometimes more than 10 optical parameters on cells at rates of approximately 10(3) cells/s, rapidly generates vast quantities of multidimensional data, which provides a considerable challenge for data analysis. We review the application of multivariate data analysis and pattern recognition techniques to flow cytometry. METHODS: Approaches were divided into two broad types depending on whether the aim was identification or clustering. Multivariate statistical approaches, supervised artificial neural networks (ANNs), problems of overlapping character distributions, unbounded data sets, missing parameters, scaling up, and estimating proportions of different types of cells comprised the first category. Classic clustering methods, fuzzy clustering, and unsupervised ANNs comprised the second category. We demonstrate the state of the art by using AFC data on marine phytoplankton populations. RESULTS AND CONCLUSIONS: Information held within the large quantities of data generated by AFC was tractable using ANNs, but for field studies the problem of obtaining suitable training data needs to be resolved, and coping with an almost infinite number of cell categories needs further research.

Flow Cytometry↗
PubMed2005

Monozygotic twins clinically discordant for scleroderma show concordance for fibroblast gene expression profiles.

OBJECTIVE: Fewer than 5% of monozygotic (MZ) and dizygotic (DZ) twin pairs are clinically concordant for systemic sclerosis (SSc), yet the majority of MZ twins are concordant for antinuclear antibodies. To discover genetic versus nongenetic molecular pathways important to the pathogenesis of SSc, we compared global gene expression patterns in twins discordant for SSc. METHODS: Total RNA from dermal fibroblasts of 15 discordant twin pairs (10 MZ and 5 DZ) and 5 normal controls were used in microarray analysis. Aberrantly expressed genes were confirmed using quantitative real-time reverse transcriptase-polymerase chain reaction. RESULTS: Lesional and nonlesional fibroblasts from SSc patients showed no significant differences in gene expression, while SSc patients had gene profiles that were significantly different from those of unaffected DZ twins and normal controls. Unaffected MZ twins, however, were not significantly different from SSc patients. Unsupervised hierarchical clustering segregated the fibroblast samples as originating from 2 major groups. Group A contained 5 discordant MZ twin pairs, 3 affected MZ twins, and 3 affected DZ twins. Group B contained all 5 normal population controls, all 5 healthy DZ twins, 2 discordant MZ twins, and 2 discordant DZ twin pairs. Normal fibroblasts incubated with serum from an SSc-affected patient or with serum from her unaffected MZ twin sister developed the increased expression of COL1A2, SPARC, and CTGF typically seen in SSc fibroblasts. CONCLUSION: These results demonstrate that dermal fibroblasts from SSc patients and from 40-50% of their genetically identical but clinically unaffected MZ twins exhibit a similar gene expression pattern which can be induced in normal fibroblasts by sera from both. Thus, a stronger genetic predisposition to SSc (than can be detected clinically) is apparent at the molecular level in skin fibroblasts.

Adult↗
PubMed2026

Effect of Tertiary Lymphoid Structures on Immune Cell Infiltration in the Tumor Microenvironment and Prognosis in Lung Adenocarcinoma.

Tertiary lymphoid structures (TLSs) modulate immune responses in various solid tumors, but their comprehensive role in lung adenocarcinoma (LUAD) remains unclear. In this study, we analyzed RNA-seq data from 539 LUAD patients in The Cancer Genome Atlas (TCGA) and microarray data from 223 samples from the Gene Expression Omnibus (GEO, GSE13213, and GSE37745). TLS signatures were evaluated via unsupervised consensus clustering based on 12 chemokine transcriptome signatures. The relationships between TLS and clinical characteristics, tumor microenvironment (TME) cell infiltration, and prognosis were assessed using ESTIMATE and CIBERSORT. A prognostic model was established using LASSO regression and validated with external datasets. Additionally, H&E and IHC analyses were performed to explore associations between intratumoral TLS density, immune-related molecular expression, and patient prognosis in LUAD. Consensus clustering of the TCGA cohort revealed two distinct LUAD patient clusters according to TLS abundance. Cluster 1 exhibited greater immune cell infiltration, more favorable prognosis, and increased expression of immune checkpoint molecules. We developed a prognostic model comprising eight survival-associated genes that act as independent prognostic factors for patient survival. H&E/IHC analyses revealed that TLS density-regardless of pathological stage-was associated with better prognosis; higher intratumoral TLS density/proportion was also related to more favorable outcomes. IHC confirmed that survival-associated genes (CD5, HLA-DMB, and P2RY13) are independent prognostic indicators in LUAD. Our study demonstrated the close relationship between TLS signatures and an active immune microenvironment, highlighting their potential as independent prognostic indicators in LUAD.

Humans↗
PubMed2026

Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.

AIMS: To preliminarily characterize metabolic molecular subtypes of cerebral thromboemboli and evaluate their clinical significance in anterior circulation acute ischemic stroke due to large vessel occlusion (AIS-LVO). METHODS: Untargeted metabolomics was performed on thromboemboli retrieved from 36 patients with anterior circulation AIS-LVO using ultra-performance coupled liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Unsupervised hierarchical clustering was employed to identify distinct metabolic molecular subtypes, and their associations with stroke etiology, radiographic severity, and functional outcomes were analyzed. RESULTS: Two distinct thrombus metabolic molecular subtypes (C1 and C2) were identified based on 12 metabolites significantly associated with both short-term (7-day &#x2206;NIHSS) and long-term (90-day mRS) functional outcomes. The C1 subtype, predominantly cardioembolic, exhibited enhanced lipid metabolism, whereas the C2 subtype, primarily atherothrombotic, demonstrated increased folate metabolism. Patients with C1 thromboemboli presented more severe admission ischemic lesions (as indicated by ASPECTS) and experienced poorer short-term and long-term outcomes. A six-metabolite signature derived from LASSO regression was identified for exploratory discrimination of thrombus metabolic subtypes, etiological subtypes, and 90-day outcomes. CONCLUSION: This preliminary exploratory study identifies two metabolically distinct thrombus molecular subtypes with clinical implications in anterior circulation AIS-LVO, providing a novel basis for risk stratification and personalized secondary prevention and warrants further investigation.

Humans↗
PubMed2006

Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics.

Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.

Animals↗
PubMed2005

CD34 expression in native human acute myelogenous leukemia blasts: differences in CD34 membrane molecule expression are associated with different gene expression profiles.

BACKGROUND: The stem cell marker CD34 is expressed by leukemia blasts only for a subset of patients with acute myelogenous leukemia (AML). It is still controversial as to whether CD34 expression (defined as at least 10-20% positive cells) has any prognostic effect in patients with AML who receive intensive chemotherapy. The present study investigated whether gene expression profiling could be used to further subclassify CD34(+) AML cell populations. METHODS: AML blasts derived from 25 patients were examined; these patients were randomly selected from a larger consecutive group of patients. CD34 protein expression was determined by flow cytometry and expressed as the percentage of positive cells. Gene expression profiles were determined by complementary DNA microarrays. RESULTS: By unsupervised hierarchical clustering our patients could be grouped into two or three major subsets depending on the methodologic approach before clustering analysis (filtering or flooring of data, respectively). However, both approaches identified a cluster characterized by high gene expression and membrane molecule level of CD34. When using the floored expression profiles, the patient cluster characterized by increased CD34 gene expression was also characterized by a high percentage of CD34(+) cells (median 82%, range 56-100%) compared with the two other major clusters (median 19%, range <1-55%), but three of four outpatients also showed a high percentage of CD34(+) cells. CONCLUSION: A major proportion of patients with AML and high CD34 expression (usually >80% CD34(+) cells; nearly all patients had >50% positive cells) showed similarities in gene expression profile. In contrast, patients with lower CD34 expression often had a profile similar to those of patients regarded as CD34(-) according to conventional criteria. Our results suggest that the possible prognostic effect of CD34 expression should be reevaluated in clinical studies using additional or alternative cutoff values to describe CD34 expression.

Adult↗
PubMed2005

Molecular changes from dysplastic nodule to hepatocellular carcinoma through gene expression profiling.

Progression of hepatocellular carcinoma (HCC) is a stepwise process that proceeds from pre-neoplastic lesions--including low-grade dysplastic nodules (LGDNs) and high-grade dysplastic nodules (HGDNs)--to advanced HCC. The molecular changes associated with this progression are unclear, however, and the morphological cues thought to distinguish pre-neoplastic lesions from well-differentiated HCC are not universally accepted. To understand the multistep process of hepato-carcinogenesis at the molecular level, we used oligo-nucleotide microarrays to investigate the transcription profiles of 50 hepatocellular nodular lesions ranging from LGDNs to primary HCC (Edmondson grades 1-3). We demonstrated that gene expression profiles can discriminate not only between dysplastic nodules and overt carcinoma but also between different histological grades of HCC via unsupervised hierarchical clustering with 10,376 genes. We identified 3,084 grade-associated genes, correlated with tumor progression, using one-way ANOVA and a one-versus-all unpooled t test. Functional assignment of these genes revealed discrete expression clusters representing grade-dependent biological properties of HCC. Using both diagonal linear discriminant analysis and support vector machines, we identified 240 genes that could accurately classify tumors according to histological grade, especially when attempting to discriminate LGDNs, HGDNs, and grade 1 HCC. In conclusion, a clear molecular demarcation between dysplastic nodules and overt HCC exists. The progression from grade 1 through grade 3 HCC is associated with changes in gene expression consistent with plausible functional consequences.

Carcinoma, Hepatocellular↗
PubMed2006

Comparison of gene expression profiles between Opisthorchis viverrini and non-Opisthorchis viverrini associated human intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary cancer in the liver, and its incidence is highest in the northeastern part of Thailand. ICCs in this region are known to be associated with infection with liver flukes, particularly Opisthorchis viverrini (OV), as well as nitrosamines from food. To clarify molecular mechanisms of ICC associated with or without liver flukes, we analyzed gene expression profiles of OV-associated ICCs from 20 Thai patients and compared their profiles with those of 20 Japanese ICCs that were not associated with OV, by means of laser microbeam microdissection and a cDNA microarray containing 27,648 genes. We identified 77 commonly upregulated genes and 325 commonly downregulated genes in the two ICC groups. Unsupervised hierarchical cluster analysis separated the 40 ICCs into two major branches almost completely according to the fluke status. The putative signature of OV-associated ICC exhibited elevated expression of genes involved in xenobiotic metabolism (UGT2B11, UGT1A10, CHST4, SULT1C1), whereas that of non-OV-associated ICC represented enhanced expression of genes related to growth factor signaling (TGFBI, PGF, IGFBP1, IGFBP3). Additional random permutation tests identified a total of 49 genes whose expression levels were significantly different between the two groups. We also identified genes associated with macroscopic type of ICCs. In conclusion, these data may not only contribute to clarification of common and OV-specific mechanisms underlying ICC, but also may serve as a starting point for the identification of novel diagnostic markers or therapeutic targets for the disease.

Adult↗
PubMed2006

Genome-wide gene expression profiling of cervical cancer in Hong Kong women by oligonucleotide microarray.

An analysis of gene expression profiles obtained from cervical cancers was performed to find those genes most aberrantly expressed. Total RNA was prepared from 29 samples of cervical squamous cell carcinoma and 18 control samples, and hybridized to Affymetrix oligonucleotide microarrays with probe sets complementary to over 20,000 transcripts. Unsupervised hierarchical clustering of the expression data readily distinguished normal cervix from cancer. Supervised analysis of gene expression data identified 98 and 139 genes that exhibited >2-fold upregulation and >2-fold downregulation, respectively, in cervical cancer compared to normal cervix. Several of the genes that were differentially regulated included SPP1 (Osteopontin), CDKN2A (p16), RPL39L, Clorf1, MAL, p11, ARS and NICE-1. These were validated by quantitative RT-PCR on an independent set of cancer and control specimens. Gene Ontology analysis showed that the list of differentially expressed genes included ones that were involved in multiple biological processes, including cell proliferation, cell cycle and protein catabolism. Immunohistochemical staining of cancer specimens further confirmed differential expression of SPP1 in cervical cancer cells vs. nontumor cells. In addition, 2 genes, CTGF and RGS1 were found to be upregulated in late stage cancer compared to early stage cancer, suggesting that they might be involved in cancer progression. The pathway analysis of expression data showed that the SPP1, VEGF, CDC2 and CKS2 genes were coordinately differentially regulated between cancer and normal. The present study is promising and provides potential new insights into the extent of expression differences underlying the development and progression of cervical squamous cell cancer. This study has also revealed several genes that may be highly attractive candidate molecular markers/targets for cervical cancer diagnosis, prognosis and therapy.

Case-Control Studies↗
PubMed2003

Gene expression in human neutrophils during activation and priming by bacterial lipopolysaccharide.

Circulating neutrophils play a key role both in the systemic inflammatory response and in complications of bacterial infection such as septic shock and septic multiple organ dysfunction syndrome. We have analyzed gene expression patterns in human neutrophils stimulated by E. coli lipopolysaccharide (LPS), with or without prior exposure to LPS, using differential display and oligonucleotide chip techniques. We identified 307 genes that were activated or repressed after treatment with LPS at 10 ng/ml and 385 genes after LPS at 100 ng/ml, compared with untreated neutrophils. The two sets included many transcription factors, cytokines, chemokines, interleukins, and surface antigens, as well as members of the toll-like receptor, Rel/NF-kappaB, and immune mediator gene families. Time course analysis showed that the early and late neutrophil responses to LPS share some common mechanisms, but many changes in gene expression are transient or late to develop. Neutrophils also showed a priming response to LPS, in which 97 genes significantly changed expression on re-exposure to lower dose LPS and were analyzed by unsupervised hierarchical clustering. These findings indicate that the neutrophil is a transcriptionally active cell responsive to environmental stimuli and capable of a complex series of both early and late changes in gene expression. Supplementary material for this article can be found on the Journal of Cellular Biochemistry website (http://jws-edci.interscience.wiley.com:8998/jpages/0730-2312/suppmat/89/v89.page.html).

Cluster Analysis↗
PubMed2005

cDNA expression profiling of chondrosarcomas: Ollier disease resembles solitary tumours and alteration in genes coding for components of energy metabolism occurs with increasing grade.

Conventional central chondrosarcomas are malignant cartilaginous tumours, occasionally arising secondary to either solitary or multiple (Ollier disease) enchondromas. Recurrences may have progressed in grade. The aims of the present study were to identify putative differences in gene expression between solitary and Ollier disease-related tumours, and to elucidate signalling pathways involved in tumour progression by genome-wide cDNA expression analysis. Arrays enriched for cartilage-specific cDNAs and genes involved in general tumourigenesis were used to analyse enchondromas (n = 3, two with Ollier disease), chondrosarcomas of different grades (n = 19, three with Ollier disease), normal resting-zone cartilage (n = 2), and chondrosarcoma cells in culture (n = 7). The arrays were analysed by unsupervised hierarchical clustering, significant analysis of microarray, and T-tests. Confirmation of data was performed by immunohistochemistry and quantitative reverse transcriptase polymerase chain reaction (RT-PCR). Ollier disease cases and solitary tumours revealed similar expression profiles, suggesting that the same signalling pathways are involved in tumourigenesis. Interestingly, JunB protein expression was significantly higher in grade I chondrosarcomas than in enchondromas (p = 0.009), which could be of diagnostic relevance. Upon chondrosarcoma progression, matrix-associated genes are down-regulated, reflecting the histology of high-grade tumours. An increase in glycolysis-associated, and a decrease in oxidative phosphorylation-related, genes was found in high-grade tumours. These findings suggest an adaptation in energy supply upon progression towards higher grade.

Adolescent↗
PubMed2006

Relationship between classic Hodgkin lymphoma and overlapping large cell lymphoma investigated by comparative expressed sequence hybridization expression profiling.

There is a diagnostic grey zone between classic Hodgkin lymphoma (cHL) and some non-Hodgkin lymphoma (NHL), including primary mediastinal B cell lymphoma, diffuse large B cell lymphoma, and anaplastic large cell lymphoma. They all have some morphological and/or phenotypic features in common. To investigate this, we undertook an expression profiling study of these lymphomas using comparative expressed sequence hybridization. This technique detects chromosomal regions that are differentially expressed between a test and a reference tissue in a manner similar to comparative genomic hybridization, and is particularly suitable when the number of informative biopsies is limited. Using this approach, we identified a unique expression profile for all lymphoma types investigated. Unsupervised hierarchical cluster analysis of the acquired data showed that cHL separates from all investigated NHLs, including ALCL-like HL. Moreover, anaplastic lymphoma kinase (ALK)-negative ALCL clustered in a separate branch together with ALCL-like HL. Thus, analysing the neoplastic cells concurrently with their microenvironment, ALK-negative ALCL and ALCL-like HL seem to be related to each other, while cHL constitutes a separate lymphoma entity.

Adolescent↗
PubMed2006

Gene expression profiling reveals intrinsic differences between T-cell acute lymphoblastic leukemia and T-cell lymphoblastic lymphoma.

BACKGROUND: T-cell acute lymphoblastic leukemia (T-ALL) and T-cell lymphoblastic lymphoma (T-LL) and are often thought to represent a spectrum of a single disease. The malignant cells in T-ALL and T-LL are morphologically indistinguishable, and they share the expression of common cell surface antigens and cytogenetic characteristics. However, despite these similarities, differences in the clinical behavior of T-ALL and T-LL are observed. PROCEDURE: We analyzed the gene expression profiles of T-ALL and T-LL samples obtained from Children's Oncology Group (COG) tumor banks using DNA arrays. Immunohistochemistry was also performed to validate the expression of selected targets. RESULTS: Unsupervised hierarchical clustering of all samples showed complete segregation of T-ALL and T-LL into distinct clusters. Next, we identified the top 201 genes that best differentiated T-ALL from T-LL using significance analysis of microarrays (SAM), a supervised statistical approach. Genes representing several functional groups were differentially expressed in T-LL and T-ALL. Prediction analysis of microarrays (PAM) identified a subset of genes, which accurately classified all 19 T-ALL and T-LL samples with an overall misclassification error rate of 0. Immunohistochemical validation of protein expression of selected genes identified by microarray analysis confirmed overexpression of MLL-1 in T-LL tumor cells compared to T-ALL and CD47 in T-ALL tumors cells when compared to T-LL. CONCLUSIONS: Despite significant similarities between the malignant T-cell precursors, clear differences in the gene expression profiles were observed between T-ALL and T-LL implying underlying differences in the biology of the two entities.

Bone Marrow↗
PubMed2004

Detection of functional modules from protein interaction networks.

Complex cellular processes are modular and are accomplished by the concerted action of functional modules (Ravasz et al., Science 2002;297:1551-1555; Hartwell et al., Nature 1999;402:C47-52). These modules encompass groups of genes or proteins involved in common elementary biological functions. One important and largely unsolved goal of functional genomics is the identification of functional modules from genomewide information, such as transcription profiles or protein interactions. To cope with the ever-increasing volume and complexity of protein interaction data (Bader et al., Nucleic Acids Res 2001;29:242-245; Xenarios et al., Nucleic Acids Res 2002;30:303-305), new automated approaches for pattern discovery in these densely connected interaction networks are required (Ravasz et al., Science 2002;297:1551-1555; Bader and Hogue, Nat Biotechnol 2002;20:991-997; Snel et al., Proc Natl Acad Sci USA 2002;99:5890-5895). In this study, we successfully isolate 1046 functional modules from the known protein interaction network of Saccharomyces cerevisiae involving 8046 individual pair-wise interactions by using an entirely automated and unsupervised graph clustering algorithm. This systems biology approach is able to detect many well-known protein complexes or biological processes, without reference to any additional information. We use an extensive statistical validation procedure to establish the biological significance of the detected modules and explore this complex, hierarchical network of modular interactions from which pathways can be inferred.

Algorithms↗
PubMed2005

Forecasting epilepsy from the heart rate signal.

Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30 s prior to seizure onset Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.

Algorithms↗
PubMed2004

[Microarrays].

Microarrays allow a simultaneous gene expression analysis of thousands of genes, providing an expression profile of the specimen investigated. Thus, this procedure is well suited to characterize the complex genetic alterations of malignant tumors. Using unsupervised hierarchical cluster analysis, characteristic expression profiles for individuals, organs and tissues, as well as for different cell types, can be identified. Molecular signatures have been observed in tumors compared to normal tissue, for different tumor stages, risk groups or response therapy. In prostate cancer, many tumor-specific gene expression alterations have been identified. Among these, the cell surface protease hepsin and alpha-methyl-acryl-CoA-racemase might gain importance as diagnostic tools. Moreover, gene expression profiles were identified which are associated with advanced tumor stage, poor differentiation or progress after radical prostatectomy. Increased expression of enzymes of steroid biosynthesis and the androgen receptor appears to be part of the molecular signature of hormone refractory prostate cancer.

Cell Transformation, Neoplastic↗