Biomedical subjects

William A Schmitt

Publications and source records attributed to William A Schmitt.

6 recordsLinked to original sources

PubMed2004

Stroop tasks reveal abnormal selective attention among psychopathic offenders.

Selective attention among offenders with psychopathy was investigated using 3 Stroop paradigms: a standard color-word (CW) Stroop, a picture-word (PW) Stroop, and a color-word Stroop in which the word and color were spatially separated (separated CW). Consistent with "overselective" attention, offenders with psychopathy displayed reduced Stroop interference on the separated CW and PW tasks relative to offenders who were not psychopathic. However, offenders with psychopathy displayed normal Stroop interference on the standard CW Stroop. Further, the reduced interference of offenders with psychopathy on the separated CW Stroop was accompanied by normal facilitation. These findings suggest a circumscribed attentional deficit in psychopathy that hinders the use of unattended information that is (a) not integrated with deliberately attended information and (b) not compatible with current goal-directed behavior.

Adult↗

PubMed2004

Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data.

The photosynthetic cyanobacterium Synechocystis sp. strain PCC 6803 uses a complex genetic program to control its physiological response to alternating light conditions. To study this regulatory program time-series experiments were conducted by exposing Synechocystis sp. to serial perturbations in light intensity. In each experiment whole-genome DNA microarrays were used to monitor gene transcription in 20-min intervals over 8- and 16-h periods. The data was analyzed using time-lagged correlation analysis, which identifies genetic interaction networks by constructing correlations between time-shifted transcription profiles with different levels of statistical confidence. These networks allow inference of putative cause-effect relationships among the organism's genes. Using light intensity as our initial input signal, we identified six groups of genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the light intensity. We expanded this network by using the average profile from each group of genes as a seed, and searching for other genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the group's average profile. The final network comprised 50 different groups containing 259 genes. Several of these gene groups possess known light-stimulated gene clusters, such as Synechocystis sp. photosystems I and II and carbon dioxide fixation pathways, while others represent novel findings in this work.

Cyanobacteria↗

PubMed2003

Prediction of transcriptional profiles of Synechocystis PCC6803 by dynamic autoregressive modeling of DNA microarray data.

Time-series profiles of gene expression generated by DNA microarrays possess sufficient information for building dynamic models of transcriptional behavior. This, however, requires properly designed experiments and sufficient independent data to validate such models. Here we report the use of AutoRegressive with eXogenous input (ARX) models to fit dynamic gene expression data obtained by subjecting cultures of the photosynthetic bacterium Synechocystis PCC6803 to consecutive light-to-dark transitions. Autoregressive with exogenous input models of appropriate complexity were selected by applying Akaike's information criterion (AIC) such as to maximize agreement between model predictions with experimental data without overfitting. These models were subsequently used to design the experimental profile of an optimal validating data set. Predictions from these models were tested in a second experiment and were found to match well with the validation data. Additionally, the models with the least error in predicting the expression profiles of the validation data set exactly match the model complexity predicted by AIC. Such models offer insights into cellular responses to environmental conditions and form the basis for hypothesizing and quantifying relationships that are presently poorly understood at the level of fundamental mechanisms.

Adaptation, Physiological↗

PubMed2003

Genomic dissection for characterization of cancerous oral epithelium tissues using transcription profiling.

Genome-wide and high-throughput functional genomic tools offer the potential of identifying disease-associated genes and dissecting disease regulatory patterns. There is a need for a set of systematic bioinformatic tools that handles efficiently a large number of variables for extracting biological meaning from experimental outputs. We present well-characterized statistical tools to discover genes that are differentially expressed between malignant oral epithelial and normal tissues in microarray experiments and to construct a robust classifier using the identified discriminatory genes. Those tools include Wilks' lambda score, error rate estimated from leave-one out cross-validation (LOOCV) and Fisher Discriminant Analysis (FDA). High Density DNA microarrays and Real Time Quantitative PCR were employed for the generation and validation of the transcription profile of the oral cancer and normal samples. We identified 45 genes that are strongly correlated with malignancy. Of the 45 genes identified, six have been previously implicated in the disease, and two are uncharacterized clones.

Gene Expression Profiling↗

PubMed2002

Mapping physiological states from microarray expression measurements.

MOTIVATION: The increasing use of DNA microarrays to probe cell physiology requires methods for visualizing different expression phenotypes and explicitly connecting individual genes to discriminating expression features. Such methods should be robust and maintain biological interpretability. RESULTS: We propose a method for the mapping of the physiological state of cells and tissues from multidimensional expression data such as those obtained with DNA microarrays. The method uses Fisher discriminant analysis to create a linear projection of gene expression measurements that maximizes the separation of different sample classes. Relative to other typical classification methods, this method provides insights into the discriminating characteristics of expression measurements in terms of the contribution of individual genes to the definition of distinct physiological states. This projection method also facilitates visualization of classification results in a reduced dimensional space. Examples from four different cases demonstrate the ability of the method to produce well-separated groups in the projection space and to identify important genes for defining physiological states. The method can be augmented to also include data from the proteomic and metabolic phenotypes and can be useful in disease diagnosis, drug screening and bioprocessing applications.

Algorithms↗

PubMed2002

Determination of minimum sample size and discriminatory expression patterns in microarray data.

MOTIVATION: Transcriptional profiling using microarrays can reveal important information about cellular and tissue expression phenotypes, but these measurements are costly and time consuming. Additionally, tissue sample availability poses further constraints on the number of arrays that can be analyzed in connection with a particular disease or state of interest. It is therefore important to provide a method for the determination of the minimum number of microarrays required to separate, with statistical reliability, distinct disease states or other physiological differences. RESULTS: Power analysis was applied to estimate the minimum sample size required for two-class and multi-class discrimination. The power analysis algorithm calculates the appropriate sample size for discrimination of phenotypic subtypes in a reduced dimensional space obtained by Fisher discriminant analysis (FDA). This approach was tested by applying the algorithm to existing data sets for estimation of the minimum sample size required for drawing certain conclusions on multi-class distinction with statistical reliability. It was confirmed that when the minimum number of samples estimated from power analysis is used, group means in the FDA discrimination space are statistically different. CONTACT: gregstep@mit.edu

Acute Disease↗