Biomedical subjects

Roberto M Cesar

Publications and source records attributed to Roberto M Cesar.

5 recordsLinked to original sources

PubMed2006

On the ternary spatial relation "between".

The spatial relation "between" is a notion which is intrinsically both fuzzy and contextual, and depends, in particular, on the shape of the objects. The literature is quite poor on this and the few existing definitions do not take into account these aspects. In particular, an object B that is in a concavity of an object A1 not visible from an object A2 is considered between A1 and A2 for most definitions, which is counter intuitive. Also, none of the definitions deal with cases where one object is much more elongated than the other. Here, we propose definitions which are based on convexity, morphological operators, and separation tools, and a fuzzy notion of visibility. They correspond to the main intuitive exceptions of the relation. We distinguish between cases where objects have similar spatial extensions and cases where one object is much more extended than the other. Extensions to cases where objects, themselves, are fuzzy and to three-dimensional space are proposed as well. The original work proposed in this paper covers the main classes of situations and overcomes the limits of existing approaches, particularly concerning nonvisible concavities and extended objects. Moreover, the definitions capture the intrinsic imprecision attached to this relation. The main proposed definitions are illustrated on real data from medical images.

Algorithms↗

PubMed2004

An environment for knowledge discovery in biology.

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported.

Computational Biology↗

PubMed2004

Automated morphometric analysis of the cat retinal alpha/Y, beta/X and delta ganglion cells using wavelet statistical moment and clustering algorithms.

Computational morphological analysis comprises the development of measures (indicators) that describe different form attributes of a neuron and provides additional parameters for classification algorithms. Our work addressed the problem of small group sizes often encountered in neuromorphological and neurophysiological research, automated classification tasks (unsupervised learning) and introduced a new morphological measure: the wavelet statistical moment. We analysed cat alpha/Y, beta/X and delta Golgi-stained retinal ganglion cells using six different shape features (circularity, 2(nd) statistical moment and entropy of Gaussian blurred images, wavelet statistical moment, number of terminations and the fractal dimension). This allowed us to compare the sensitivity of the methods in uniquely describing morphological attributes of these cells.

Algorithms↗

PubMed2003

A comparative analysis of structure and spatial distribution of decorin in human leiomyoma and normal myometrium.

Leiomyoma is a benign smooth muscle tumor of the uterus that affects many women in active reproductive life. It is composed by bundles of smooth muscle cells surrounded by extracellular matrix. We have recently shown that the glycosylation of extracellular matrix proteoglycans is modified in leiomyoma: increased amounts of galactosaminoglycans with structural modifications are present. The data here presented show that decorin is present in both normal myometrium and leiomyoma but tumoral decorin is glycosylated with longer galactosaminoglycan side chains. Furthermore, these chains contain a higher ratio D-glucuronate/L-iduronate, as compared to normal tissue. To determine if these changes in proteoglycan glycosylation correlates with modifications in the extracellular matrix organization, we compared the general structural architecture of leiomyoma to normal myometrium. By histochemical and immunofluorescence methods, we found a reorganization of muscle fibers and extracellular matrix, with changes in the distribution of glycoproteins, proteoglycans, and collagen. Thin reticular fibers, possibly composed by types I and III collagen, were replaced by thick fibers, possibly richer in type I collagen. Type I collagen colocalized with decorin both in leiomyoma and normal myometrium, in contrast to type IV collagen that did not. The relative amount of decorin was increased and the distribution of decorin and collagen was totally modified in the tumor, as compared to the normal myometrium. These findings reveal that not only decorin structure is modified in leiomyoma but also the tissue architecture changed, especially concerning extracellular matrix.

Adult↗

PubMed2002

Inference from clustering with application to gene-expression microarrays.

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selected by hand to test the separability of expected types of biological expression patterns. Alternatively, the model can be seeded by real data to test the expected precision of that output or the extent of improvement in precision that replication could provide. In the latter case, a clustering algorithm is used to form clusters, and the model is seeded with the means and variances of these clusters. Other algorithms are then tested relative to the seeding algorithm. Results are averaged over various seeds. Output includes error tables and graphs, confusion matrices, principal-component plots, and validation measures. Five algorithms are studied in detail: K-means, fuzzy C-means, self-organizing maps, hierarchical Euclidean-distance-based and correlation-based clustering. The toolbox is applied to gene-expression clustering based on cDNA microarrays using real data. Expression profile graphics are generated and error analysis is displayed within the context of these profile graphics. A large amount of generated output is available over the web.

Computational Biology↗