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Biomedical subjects

Michael L Bittner

Publications and source records attributed to Michael L Bittner.

6 recordsLinked to original sources
PubMed2003

Differential responses of stress genes to low dose-rate gamma irradiation.

In the past, most mechanistic studies of ionizing radiation response have employed very large doses, then extrapolated the results down to doses relevant to human exposure. It is becoming increasingly apparent, however, that this does not give an accurate or complete picture of the effects of most environmental exposures, which tend to be of low dose and protracted over time. We have initiated direct studies of low dose exposures, and using the relatively responsive ML-1 cell line, have shown that changes in gene expression can be triggered by doses of gamma-rays of 10 cGy and less in human cells. We have now extended these studies to investigate the effects on gene induction of reducing the rate of irradiation. In the ML-1 human myeloid leukemia cell line, we have found that reducing the dose rate over three orders of magnitude results in some protection against the induction of apoptosis, but still causes linear induction of the p53-regulated genes CDKN1A, GADD45A, and MDM2 between 2 and 50 cGy. Reducing the rate of exposure reduces the magnitude of induction of CDKN1A and GADD45A, but not the magnitude or duration of cell cycle delay. In contrast, MDM2 is induced to the same extent regardless of the rate of dose delivery. Microarray analysis has identified additional low dose-rate-inducible genes, and indicates the existence of two general classes of low dose-rate responders in ML-1. One group of genes is induced in a dose rate-dependent fashion, similar to GADD45A and CDKN1A. Functional annotation of this gene cluster indicates a preponderance of genes with known roles in apoptosis regulation. Similarly, a group of genes with dose rate-independent induction, such as seen for MDM2, was also identified. The majority of genes in this group are involved in cell cycle regulation. This apparent differential regulation of stress signaling pathways and outcomes in response to protracted radiation exposure has implications for carcinogenesis and risk assessment, and could not have been predicted from classical high dose studies.

Apoptosis↗
PubMed2002

Strong feature sets from small samples.

For small samples, classifier design algorithms typically suffer from overfitting. Given a set of features, a classifier must be designed and its error estimated. For small samples, an error estimator may be unbiased but, owing to a large variance, often give very optimistic estimates. This paper proposes mitigating the small-sample problem by designing classifiers from a probability distribution resulting from spreading the mass of the sample points to make classification more difficult, while maintaining sample geometry. The algorithm is parameterized by the variance of the spreading distribution. By increasing the spread, the algorithm finds gene sets whose classification accuracy remains strong relative to greater spreading of the sample. The error gives a measure of the strength of the feature set as a function of the spread. The algorithm yields feature sets that can distinguish the two classes, not only for the sample data, but for distributions spread beyond the sample data. For linear classifiers, the topic of the present paper, the classifiers are derived analytically from the model, thereby providing an enormous savings in computation time. The algorithm is applied to cancer classification via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the algorithm is used to find gene sets whose expressions can be used to classify BRCA1 and BRCA2 tumors.

Breast Neoplasms↗
PubMed2002

Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices.

MOTIVATION: The simplest level of statistical analysis of cancer associated gene expression matrices is aimed at finding consistently up- or down-regulated genes within a given set of tumor samples. Considering the high level of gene expression diversity detected in cancer, one needs to assess the probability that the consistent mis-regulation of a given gene is due to chance. Furthermore, it is important to determine the required sample number that will ensure the meaningful statistical analysis of massively parallel gene expression measurements. RESULTS: The probability of consistent mis-regulation is calculated in this paper for binarized gene expression data, using combinatorial considerations. For practical purposes, we also provide a set of accurate approximate formulas for determining the same probability in a computationally less intensive way. When the pool of mis-regulatable genes is restricted, the probability of consistent mis-regulation can be overestimated. We show, however, that this effect has little practical consequences for cancer associated gene expression measurements published in the literature. Finally, in order to aid experimental design, we have provided estimates on the required sample number that will ensure that the detected consistent mis-regulation is not due to chance. Our results suggest that less than 20 sufficiently diverse tumor samples may be enough to identify consistently mis-regulated genes in a statistically significant manner. AVAILABILITY: An implementation using Mathematica (tm) of the main equation of the paper, (4), is available at www.me.chalmers.se/~mwahde/bioinfo.html.

Algorithms↗
PubMed2002

Ratio statistics of gene expression levels and applications to microarray data analysis.

MOTIVATION: Expression-based analysis for large families of genes has recently become possible owing to the development of cDNA microarrays, which allow simultaneous measurement of transcript levels for thousands of genes. For each spot on a microarray, signals in two channels must be extracted from their backgrounds. This requires algorithms to extract signals arising from tagged mRNA hybridized to arrayed cDNA locations and algorithms to determine the significance of signal ratios. RESULTS: This paper focuses on estimation of signal ratios from the two channels, and the significance of those ratios. The key issue is the determination of whether a ratio is significantly high or low in order to conclude whether the gene is upregulated or downregulated. The paper builds on an earlier study that involved a hypothesis test based on a ratio statistic under the supposition that the measured fluorescent intensities subsequent to image processing can be assumed to reflect the signal intensities. Here, a refined hypothesis test is considered in which the measured intensities forming the ratio are assumed to be combinations of signal and background. The new method involves a signal-to-noise ratio, and for a high signal-to-noise ratio the new test reduces (with close approximation) to the original test. The effect of low signal-to-noise ratio on the ratio statistics constitutes the main theme of the paper. Finally, and in this vein, a quality metric is formulated for spots. This measure can be used to decide whether or not a spot ratio should be deleted, or to adjust various measurements to reflect confidence in the quality of the measurement. CONTACT: e-dougherty@tamu.edu

Cell Line↗
PubMed2002

Simulation of cDNA microarrays via a parameterized random signal model.

cDNA microarrays provide simultaneous expression measurements for thousands of genes that are the result of processing images to recover the average signal intensity from a spot composed of pixels covering the area upon which the cDNA detector has been put down. The accuracy of the signal measurement depends on using an appropriate algorithm to process the images. This includes determining spot locations and processing the data in such a way as to take into account spot geometry, background noise, and various kinds of noise that degrade the signal. This paper presents a stochastic model for microarray images. There are over 20 model parameters, each governed by a probability distribution, that control the signal intensity, spot geometry, spot drift, background effects, and the many kinds of noise that affect microarray images owing to the manner in which they are formed. The model can be used to analyze the performance of image algorithms designed to measure the true signal intensity because the ground truth (signal intensity) for each spot is known. The levels of foreground noise, background noise, and spot distortion can be set, and algorithms can be evaluated under varying conditions.

Algorithms↗