Study of gene expression in thyrotropin-stimulated thyroid cells by cDNA expression array: ID3 transcription modulating factor as an early response protein and tumor marker in thyroid carcinomas.

Induction of cell proliferation by mitogen or growth factor stimulation leads to the specific induction or repression of a large number of genes. To identify genes differentially regulated by the cAMP-dependent transduction pathway, which is poorly characterized so far, we used the cDNA expression array technology. Hybridizations of Atlas human cDNA expression arrays with (32)P-labeled cDNA probes derived from control or thyrotropin (TSH)-stimulated dog thyrocytes in primary culture generated expression profiles of hundreds of genes simultaneously. Among the genes that displayed modified expression, we selected the transcription factor ID3, whose expression was increased by a cAMP-dependent stimulus. ID3 overexpression after TSH stimulation was first verified by Northern blotting analysis, and its mRNA regulation was then investigated in response to a variety of agents acting on thyrocyte proliferation and/or differentiation. We show that: (1) ID3 mRNA induction was stronger after stimulation of the cAMP cascade, but was not restricted to this signaling pathway, as phorbol myristate ester (TPA) and insulin also stimulated mRNA accumulation; (2) in contrast, powerful mitogens for thyroid cells, epidermal growth factor and hepatocyte growth factor, did not significantly modify ID3 mRNA levels; (3) ID3 protein levels closely parallelled mRNA levels, as revealed by immunofluorescence experiments showing a nuclear signal regulated by TSH; (4) in papillary thyroid carcinomas, ID3 mRNA was downregulated. Our results suggest that ID3 expression might be more related to the differentiating process induced by TSH than to the proliferative action of this hormone.

Recuperação de informação de música e dados ID3: possíveis aplicações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fuzzy-Rough Feature Significance for Fuzzy Decision Trees

R. Jensen and Q. Shen, 'Fuzzy-Rough Feature Significance for Fuzzy Decision Trees,' in Proceedings of the 2005 UK Workshop on Computational Intelligence, pp. 89-96, 2005.

Building a person home behavior model based on data from smart house sensors 2015

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Yet another induction algorithm

Inducing general functions from specific training examples is a central problem in the machine learning. Using sets of If-then rules is the most expressive and readable manner. To find If-then rules, many induction algorithms such as ID3, AQ, CN2 and their variants, were proposed. Sequential covering is the kernel technique of them. To avoid testing all possible selectors, Entropy gain is used to select the best attribute in ID3. Constraint of the size of star was introduced in AQ and beam search was adopted in CN2. These methods speed up their induction algorithms but many good selectors are filtered out. In this work, we introduce a new induction algorithm that is based on enumeration of all possible selectors. Contrary to the previous works, we use pruning power to reduce irrelative selectors. But we can guarantee that no good selectors are filtered out. Comparing with other techniques, the experiment results demonstrate
that the rules produced by our induction algorithm have high consistency and simplicity.

Methodological shifts in ratio-based modelling of corporate collapse

The year 1968 saw a major shift from univariate to multivariate methodological approaches to ratio-based modelling of corporate collapse. This was facilitated by the introduction of a new statistical tool called Multiple Discriminant Analysis (MDA). However, it did not take long before other statistical tools were developed. The primary objective for developing these tools was to enable deriving models that would at least do as good a job asMDA, but rely on fewer assumptions. With the introduction of new statistical tools, researchers became pre-occupied with testing them in signalling collapse. lLTUong the ratio-based approaches were Logit analysis, Neural Network analysis, Probit analysis, ID3, Recursive Partitioning Algorithm, Rough Sets analysis, Decomposition analysis, Going Concern Advisor, Koundinya and Purl judgmental approach, Tabu Search and Mixed Logit analysis. Regardless of which methodological approach was chosen, most were compared to MDA. This paper reviews these various approaches. Emphasis is placed on how they fared against MDA in signalling corporate collapse.

Keyword extraction for text categorization

Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.

An illustration of variable precision rough set theory : The gender classification of the European barn swallow (Hirundo rustica)

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.

Finding coverage using incremental attribute combinations

Coverage is the range that covers only positive samples in attribute (or feature) space. Finding coverage is the kernel problem in induction algorithms because of the fact that coverage can be used as rules to describe positive samples. To reflect the characteristic of training samples, it is desirable that the large coverage that cover more positive samples. However, it is difficult to find large coverage, because the attribute space is usually very high dimensionality. Many heuristic methods such as ID3, AQ and CN2 have been proposed to find large coverage. A robust algorithm also has been proposed to find the largest coverage, but the complexities of time and space are costly when the dimensionality becomes high. To overcome this drawback, this paper proposes an algorithm that adopts incremental feature combinations to effectively find the largest coverage. In this algorithm, the irrelevant coverage can be pruned away at early stages because potentially large coverage can be found earlier. Experiments show that the space and time needed to find the largest coverage has been significantly reduced.

Unveiling novel genes upregulated by both rhBMP2 and rhBMP7 during early osteoblastic transdifferentiation of C2C12 cells

Abstract Findings We set out to analyse the gene expression profile of pre-osteoblastic C2C12 cells during osteodifferentiation induced by both rhBMP2 and rhBMP7 using DNA microarrays. Induced and repressed genes were intercepted, resulting in 1,318 induced genes and 704 repressed genes by both rhBMP2 and rhBMP7. We selected and validated, by RT-qPCR, 24 genes which were upregulated by rhBMP2 and rhBMP7; of these, 13 are related to transcription (Runx2, Dlx1, Dlx2, Dlx5, Id1, Id2, Id3, Fkhr1, Osx, Hoxc8, Glis1, Glis3 and Cfdp1), four are associated with cell signalling pathways (Lrp6, Dvl1, Ecsit and PKCδ) and seven are associated with the extracellular matrix (Ltbp2, Grn, Postn, Plod1, BMP1, Htra1 and IGFBP-rP10). The novel identified genes include: Hoxc8, Glis1, Glis3, Ecsit, PKCδ, LrP6, Dvl1, Grn, BMP1, Ltbp2, Plod1, Htra1 and IGFBP-rP10. Background BMPs (bone morphogenetic proteins) are members of the TGFβ (transforming growth factor-β) super-family of proteins, which regulate growth and differentiation of different cell types in various tissues, and play a critical role in the differentiation of mesenchymal cells into osteoblasts. In particular, rhBMP2 and rhBMP7 promote osteoinduction in vitro and in vivo, and both proteins are therapeutically applied in orthopaedics and dentistry. Conclusion Using DNA microarrays and RT-qPCR, we identified both previously known and novel genes which are upregulated by rhBMP2 and rhBMP7 during the onset of osteoblastic transdifferentiation of pre-myoblastic C2C12 cells. Subsequent studies of these genes in C2C12 and mesenchymal or pre-osteoblastic cells should reveal more details about their role during this type of cellular differentiation induced by BMP2 or BMP7. These studies are relevant to better understanding the molecular mechanisms underlying osteoblastic differentiation and bone repair.

MicroRNA miR-199a-5p regulates smooth muscle cell proliferation and morphology by targeting Wnt2 signaling pathway

MicroRNA miR-199a-5p impairs tight junction formation leading to increased urothelial permeability in bladder pain syndrome. Now using transcriptome analysis in urothelial TEU-2 cells we implicate it in the regulation of cell cycle, cytoskeleton remodeling, TGF and Wnt signaling pathways. MiR-199a-5p is highly expressed in the smooth muscle layer of the bladder and we altered its levels in bladder smooth muscle cells (SMC) to validate the pathway analysis. Inhibition of miR-199a-5p with antimiR increased SMC proliferation, reduced cell size and up-regulated miR-199a-5p targets, including Wnt2. Overexpression of Wnt2 protein or treating SMCs with recombinant Wnt2 closely mimicked the miR-199a-5p inhibition, whereas down-regulation of Wnt2 in antimiR-expressing SMCs with shRNA restored cell phenotype and proliferation rates. Overexpression of miR-199a-5p in the bladder SMCs significantly increased cell size and up-regulated SM22, SM alpha-actin and SM myosin heavy chain mRNA and protein levels. These changes, as well as increased expression of ACTG2, TGFB1I1, and CDKN1A were mediated by up-regulation of smooth muscle-specific transcriptional activator myocardin at mRNA and protein levels. Myocardin-related transcription factor (MRTF-A) downstream targets Id3 and MYL9 were also induced. Up-regulation of myocardin was accompanied by down-regulation of Wnt-dependent inhibitory Kruppel-like transcription factor 4 (KLF4) in miR-199a-5p overexpressing cells. In contrast, KLF4 was induced in antimiR-expressing cells following the activation of Wnt2 signaling, leading to repression of myocardin-dependent genes. MiR-199a-5p plays a critical role in the Wnt2-mediated regulation of proliferative and differentiation processes in the smooth muscle and may behave as a key modulator of smooth muscle hypertrophy, relevant for organ remodeling.

BMP2-mediated alteration in the developmental pathway of fetal mouse brain cells from neurogenesis to astrocytogenesis

We show that when telencephalic neural progenitors are briefly exposed to bone morphogenetic protein 2 (BMP2) in culture, their developmental fate is changed from neuronal cells to astrocytic cells. BMP2 significantly reduced the number of cells expressing microtubule-associated protein 2, a neuronal marker, and cells expressing nestin, a marker for undifferentiated neural precursors, but BMP2 increased the number of cells expressing S100-β, an astrocytic marker. In telencephalic neuroepithelial cells, BMP2 up-regulated the expression of negative helix–loop–helix (HLH) factors Id1, Id3, and Hes-5 (where Hes is homologue of hairy and Enhancer of Split) that inhibited the transcriptional activity of neurogenic HLH transcription factors Mash1 and neurogenin. Ectopic expression of either Id1 or Id3 (where Id is inhibitor of differentiation) inhibited neurogenesis of neuroepithelial cells, suggesting an important role for these HLH proteins in the BMP2-mediated changes in the neurogenic fate of these cells. Because gliogenesis in the brain and spinal cord, derived from implanted neural stem cells or induced by injury, is responsible for much of the failure of neuronal regeneration, this work may lead to a therapeutic strategy to minimize this problem.

A region of consistent deletion in neuroblastoma maps within human chromosome 1p36.2-36.3.

Deletion of the short arm of human chromosome 1 is the most common cytogenetic abnormality observed in neuroblastoma. To characterize the region of consistent deletion, we performed loss of heterozygosity (LOH) studies on 122 neuroblastoma tumor samples with 30 distal chromosome 1p polymorphisms. LOH was detected in 32 of the 122 tumors (26%). A single region of LOH, marked distally by D1Z2 and proximally by D1S228, was detected in all tumors demonstrating loss. Also, cells from a patient with a constitutional deletion of 1p36, and from a neuroblastoma cell line with a small 1p36 deletion, were analyzed by fluorescence in situ hybridization. Cells from both sources had interstitial deletions of 1p36.2-36.3 which overlapped the consensus region of LOH defined by the tumors. Interstitial deletion in the constitutional case was confirmed by allelic loss studies using the panel of polymorphic markers. Four proposed candidate genes--DAN, ID3 (heir-1), CDC2L1 (p58), and TNFR2--were shown to lie outside of the consensus region of allelic loss, as defined by the above deletions. These results more precisely define the location of a neuroblastoma suppressor gene within 1p36.2-36.3, eliminating 33 centimorgans of proximal 1p36 from consideration. Furthermore, a consensus region of loss, which excludes the four leading candidate genes, was found in all tumors with 1p36 LOH.