A hierarchical unsupervised growing neural network for
  clustering gene expression patterns

Herrero, Javier; Valencia, Alfonso; Dopazo, Joaquı́n

doi:10.1093/bioinformatics/17.2.126

Cited by 532 publications

(314 citation statements)

References 24 publications

Supporting

Mentioning

311

Contrasting

Unclassified

Order By: Relevance

“…The cluster was displayed using SOTAARRAY (Herrero et al, 2001) (software available at http://gepas.bioinfo.cipf.es). IHC results were represented by range of color from green to red, the lowest and the highest percentage of positive cells for each marker, respectively.…”

Section: Discussionmentioning

confidence: 99%

Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes

et al. 2007

View full text Add to dashboard Cite

Five breast cancer subtypes have been described in sporadic breast cancer (SBC) using expression arrays: basal-like, ERBB2, normal breast-like, luminal A and B. These molecular subtypes show different genomic aberration patterns (GAPs). Recently, our group described these breast cancer subtypes in 50 non-BRCA1/2 familial tumors using immunohistochemistry assays. We extended this study to the other classes of familial breast cancer (FBC), including 62 tumors (18 BRCA1, 16 BRCA2 and 28 non-BRCA1/2), with the same panel of 25 immunohistochemical (IHC) markers and histological grade obtaining a similar classification. We combined these data with results generated by a 1 Mb BAC array-based CGH study to evaluate the genomic aberrations of each group. We found that BRCA1-related tumors are preferentially basal-like, whereas non-BRCA1/2 familial tumors are mainly luminal A subtype. We described distinct GAPs related to each IHC subtype. Basal tumors had a greater number of gains/losses, while luminal B tumors had more high-level DNA amplifications. Our data are similar to those obtained in SBC studies, highlighting the existence of distinct genetic pathways of tumor evolution, common to both SBC and FBC.

show abstract

Section: Discussionmentioning

confidence: 99%

Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes

et al. 2007

View full text Add to dashboard Cite

show abstract

“…Although in this case positive regulation of Ret In a recent study (Gujral et al 2012), breast cancer samples were subjected to "reversephase" protein microarrays (Sevecka and MacBeath 2006) and phosphorylation or total protein levels of multiple signaling molecules were tested. The resulted data were analyzed by unsupervised hierarchical clustering (Herrero et al 2001), revealing 12 major clusters of correlating proteins. Interestingly, c-Met RTK phosphorylated on Tyr1349 was clustered together with the total protein level of Axl, which is a structurally independent RTK.…”

Section: Rtk Pathway Cross Talk: Fine Balances At Multiple Levelsmentioning

confidence: 99%

Complexity of Receptor Tyrosine Kinase Signal Processing

Volinsky

Kholodenko

2013

Cold Spring Harbor Perspectives in Biology

View full text Add to dashboard Cite

Our knowledge of molecular mechanisms of receptor tyrosine kinase (RTK) signaling advances with ever-increasing pace. Yet our understanding of how the spatiotemporal dynamics of RTK signaling control specific cellular outcomes has lagged behind. Systems-centered experimental and computational approaches can help reveal how overlapping networks of signal transducers downstream of RTKs orchestrate specific cell-fate decisions. We discuss how RTK network regulatory structures, which involve the immediate posttranslational and delayed transcriptional controls by multiple feed forward and feedback loops together with pathway cross talk, adapt cells to the combinatorial variety of external cues and conditions. This intricate network circuitry endows cells with emerging capabilities for RTK signal processing and decoding. We illustrate how mathematical modeling facilitates our understanding of RTK network behaviors by unraveling specific systems properties, including bistability, oscillations, excitable responses, and generation of intricate landscapes of signaling activities.

show abstract

“…All available tumors were included. Hierarchical clustering and Tree View from GEPAS (Herrero et al, 2001) were used to visualize the predictor results.…”

Section: Functional Profiling Of Dedifferentiation and Aggressivenessmentioning

confidence: 99%

Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information

et al. 2007

View full text Add to dashboard Cite

Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with >2-fold difference in absolute values and false discovery rate of o0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-b signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.

show abstract

A hierarchical unsupervised growing neural network for clustering gene expression patterns

Abstract: A server running the program can be found at: http://bioinfo.cnio.es/sotarray.

Cited by 532 publications

References 24 publications

Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes

Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes

Complexity of Receptor Tyrosine Kinase Signal Processing

Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information

Contact Info

Product

Resources

About