Biomedical subjects

Saurabh Sinha

Publications and source records attributed to Saurabh Sinha.

6 recordsLinked to original sources

PubMed2003

YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation.

A fundamental challenge facing biologists is to identify DNA binding sites for unknown regulatory factors, given a collection of genes believed to be coregulated. The program YMF identifies good candidates for such binding sites by searching for statistically overrepresented motifs. More specifically, YMF enumerates all motifs in the search space and is guaranteed to produce those motifs with greatest z-scores. This note describes the YMF web software, available at http://bio.cs.washington.edu/software.html.

Algorithms↗

PubMed2003

Discriminative motifs.

This paper takes a new view of motif discovery, addressing a common problem in existing motif finders. A motif is treated as a feature of the input promoter regions that leads to a good classifier between these promoters and a set of background promoters. This perspective allows us to adapt existing methods of feature selection, a well-studied topic in machine learning, to motif discovery. We develop a general algorithmic framework that can be specialized to work with a wide variety of motif models, including consensus models with degenerate symbols or mismatches, and composite motifs. A key feature of our algorithm is that it measures overrepresentation while maintaining information about the distribution of motif instances in individual promoters. The assessment of a motif's discriminative power is normalized against chance behaviour by a probabilistic analysis. We apply our framework to two popular motif models and are able to detect several known binding sites in sets of co-regulated genes in yeast.

Algorithms↗

PubMed2003

A probabilistic method to detect regulatory modules.

MOTIVATION: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. RESULTS: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.

Algorithms↗

PubMed2002

Discovery of novel transcription factor binding sites by statistical overrepresentation.

Understanding the complex and varied mechanisms that regulate gene expression is an important and challenging problem. A fundamental sub-problem is to identify DNA binding sites for unknown regulatory factors, given a collection of genes believed to be co-regulated. We discuss a computational method that identifies good candidates for such binding sites. Unlike local search techniques such as expectation maximization and Gibbs samplers that may not reach a global optimum, the method discussed enumerates all motifs in the search space, and is guaranteed to produce the motifs with greatest z-scores. We discuss the results of validation experiments in which this algorithm was used to identify candidate binding sites in several well studied regulons of Saccharomyces cerevisiae, where the most prominent transcription factor binding sites are largely known. We then discuss the results on gene families in the functional and mutant phenotype catalogs of S.cerevisiae, where the algorithm suggests many promising novel transcription factor binding sites. The program is available at http://bio.cs.washington.edu/software.html.

Algorithms↗

PubMed2002

Measurements of water diffusion and T1 values in peritumoural oedematous brain.

Using quantitative MR imaging, values for the mean water diffusivity ( ), the diffusion anisotropy and the longitudinal relaxation time (T1) were measured for tumour, oedematous and normal brain in a group of treatment-naive patients with high-grade glioma and low-grade meningioma. The mean values of and T1 for enhancing tumour and oedematous brain were significantly higher in high-grade glioma than meningioma, while the diffusion anisotropy was reduced. Values of and T1 were also positively correlated in oedematous brain in both pathologies. There was, however, no clear evidence of similar correlations in apparently normal contralateral white matter. Such results illustrate the potential of MR imaging to improve not only the characterization of brain oedema, but also the monitoring of treatment response.

Aged↗

PubMed2002

Diffusion tensor MR imaging of high-grade cerebral gliomas.

BACKGROUND AND PURPOSE: Optimizing high-grade glioma treatment requires the delineation of edematous and normal brain from tumor, perhaps by using potential differences in the absolute diffusion parameters of water. Our purpose was to determine whether mean diffusivity and diffusion anisotropic MR imaging data help in this differentiation. METHODS: Nine patients with high-grade cerebral glioblastoma underwent contrast-enhanced structural and diffusion tensor MR imaging before therapy. Tumor, edematous brain, and apparently normal white matter regions were determined on T2-weighted and contrast-enhanced T1-weighted structural images. Fractional anisotropy (FA) and were measured in each tissue type. Differences in these values among the tissue types were assessed with a standard analysis of variance. RESULTS: was highest in the necrotic tumor core (1825.38 +/-404.06) x 10(-6) mm(2)/s, followed by edematous brain (1411.23 +/- 322.31) x 10(-6) mm(2)/s, enhancing tumor core (1308.67 +/- 292.50) x 10(-6) mm(2)/s, enhancing tumor margin (1229.80 +/- 206.80) x 10(-6) mm(2)/s, and normal brain (731.53 +/- 35.21) x 10(-6) mm(2)/s. FA was highest in normal brain (0.47 +/- 0.08) and lowest in the necrotic core (0.09 +/- 0.03). was significantly different in enhancing tumor margins and edematous brain in all patients; FA was significantly different in only seven. These values were significantly different from those of normal brain in all cases in which they were measurable. CONCLUSION: values can be used to differentiate normal white matter, edematous brain, and enhancing tumor margins. Diffusion anisotropic data added no benefit to tissue differentiation. Further studies are required to determine if a value that corresponds to the limit of tumor invasion can be identified.

Adult↗