bluuebunny/crossref_metadata_2025_split huggingface dataset:
Speech and language skills and cognitive functioning in children with Apert syndrome: A pilot study
Authors: Shipster C. , Hearst D. , Dockrell J. E. , Kilby E. , Hayward R. 
Published: September 2002

AbstractThere are few studies that report findings on the speech and language characteristics of Apert syndrome and little is known about the cognitive profile of the syndrome. The current study addresses this gap and explores speech, language, resonance/voice, attention oro‐motor and cognitive skills in a group of 10 children (4;1‐5;11) with Apert syndrome. The speech and language battery included: the CELF‐Pre‐school, the PLS‐3, the Vocal Profile Analysis, GOS.SP.ASS, PACS; and the Brodsky Drooling Scale. Subscales of the BAS II‐Early Years Version were used to assess cognition. Data were also collected on other factors that could influence developmental outcome such as audiological history and management; occlusion/dentition; respiratory problems and management; neuroanatomical abnormalities; the number and nature of cranial surgeries; and the occurrence of raised intracranial pressure. All children for whom a Performance IQ was obtained (n = 8) had abilities within the average range and IQ scores were considerably higher than those reported in previous studies. Eight children had moderate or severe language difficulties and expressive language difficulties were the most frequent. These language difficulties were not associated with a general cognitive deficit. All the children had problems with attention, speech and oro‐motor skills. Nine had abnormal voice. In addition, a range of other associated factors that could affect functioning were identified. The discrepancies between the current study and previous investigations are outlined. Parameters for assessment are considered. The implications of these findings for valid assessments of children with Apert syndrome are discussed. Multidisciplinary assessment of children with Apert syndrome across a broad range of dimensions is recommended to obtain a profile of each child's strengths and weaknesses to ensure that appropriate educational placements and early interventions are implemented. Considering patterns of development over time at key ages is also argued to be of central importance.
Genome structures embossed by oligonucleotide-stickiness
Authors: Nishigaki Koichi , Saito Ayumu 
Published: September 2002

Abstract
Motivation: An unmanageably large amount of data on genome sequences is accumulating, prompting researchers to develop new methods to analyze them. We have devised a novel method designated oligostickiness, a measure roughly proportional to the binding affinity of an oligonucleotide to a DNA of interest, in order to analyze genome sequences as a whole.
Results: Fifteen representative genomes such as Bacillus subtilis, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, H. sapiens and others were analyzed by this method using more than 50 probe dodecanucleotides, offering the following findings: (i) Genome sequences can be specifically featured by way of oligostickiness maps. (ii) Oligostickiness analysis, which is similar to but more informative than (G + C) content or repetitive sequence analysis, can reveal intra-genomic structures such as mosaic structures (E. coli and B. subtilis) and highly sticky/non-sticky regions of biological meanings. (iii) Some probe oligonucleotides such as dC12 and dT12 can be used for classifying genomes, clearly discriminating prokaryotes and eukaryotes. (iv) Based on global oligostickiness, which is the average value of the local oligostickinesses, the features of a genome could be visualized in spider web mode. The pattern of a spider web as well as a set of oligostickiness maps is highly characteristic to each genome or chromosome. Thus, we called it as chromosome texture, leading to a finding that all the chromosomes contained in a cell, so far investigated, have a common texture.
Availability: Oligostickinesses maps used in this work are available at http://gp.fms.saitama-u.ac.jp/
Contact: koichi@fms.saitama-u.ac.jp
* To whom correspondence should be addressed.
Pooled library tissue tags for EST-based gene discovery
Authors: Gavin A. J. , Scheetz T. E. , Roberts C. A. , O'Leary B. , Braun T. A. , Sheffield V. C. , Soares M. B. , Robinson J. P. , Casavant T. L. 
Published: September 2002

Abstract
Motivation: In gene discovery projects based on EST sequencing, effective post-sequencing identification methods are important in determining tissue sources of ESTs within pooled cDNA libraries. In the past, such identification efforts have been characterized by higher than necessary failure rates due to the presence of errors within the subsequence containing the oligo tag intended to define the tissue source for each EST.
Results: A large-scale EST-based gene discovery program at The University of Iowa has led to the creation of a unique software method named UITagCreator usable in the creation of large sets of synthetic tissue identification tags. The identification tags provide error detection and correction capability and, in conjunction with automated annotation software, result in a substantial improvement in the accurate identification of the tissue source in the presence of sequencing and base-calling errors. These identification rates are favorable, relative to past paradigms.
Availability: The UITagCreator source code and installation instructions, along with detection software usable in concert with created tag sets, is freely available at http://genome.uiowa.edu/pubsoft/software.html
Contact: tomc@eng.uiowa.edu
* To whom correspondence should be addressed.
Identification of regulatory elements using a feature
selection method
Authors: Keleş Sündüz , van der Laan Mark , Eisen Michael B. 
Published: September 2002

Abstract
Motivation: Many methods have been described to identify regulatory motifs in the transcription control regions of genes that exhibit similar patterns of gene expression across a variety of experimental conditions. Here we focus on a single experimental condition, and utilize gene expression data to identify sequence motifs associated with genes that are activated under this experimental condition. We use a linear model with two-way interactions to model gene expression as a function of sequence features (words) present in presumptive transcription control regions. The most relevant features are selected by a feature selection method called stepwise selection with monte carlo cross validation. We apply this method to a publicly available dataset of the yeast Saccharomyces cerevisiae, focussing on the 800 basepairs immediately upstream of each gene's translation start site (the upstream control region (UCR)).
Results: We successfully identify regulatory motifs that are known to be active under the experimental conditions analyzed, and find additional significant sequences that may represent novel regulatory motifs. We also discuss a complementary method that utilizes gene expression data from a single microarray experiment and allows averaging over variety of experimental conditions as an alternative to motif finding methods that act on clusters of co-expressed genes.
Availability: The software is available upon request from the first author or may be downloaded from http://www.stat.berkeley.edu/~sunduz.
Contact: keles@stat.berkeley.edu
* To whom correspondence should be addressed.
Geometry of gene expression dynamics
Authors: Rifkin S. A. , Kim J. 
Published: September 2002

Abstract
Motivation: A gene expression trajectory moves through a high dimensional space where each axis represents the mRNA abundance of a different gene. Genome wide gene expression has a dynamic structure, especially in studies of development and temporal response. Both visualization and analyses of such data require an explicit attention to the temporal structure.
Results: Using three cell cycle trajectories from Saccharomyces cerevisiae to illustrate, we present several techniques which reveal the geometry of the data. We import phase-delay time plots from chaotic systems theory as a dynamic data visualization device and show how these plots capture important aspects of the trajectories. We construct an objective function to find an optimal two-dimensional projection of the cell cycle, demonstrate that the system returns to this plane after three different initial perturbations, and explore the conditions under which this geometric approach outperforms standard approaches such as singular value decomposition and Fourier analysis. Finally, we show how a geometric analysis can isolate distinct parts of the trajectories, in this case the initial perturbation versus the cell cycle.
Contact: junhyong.kim@yale.edu
* To whom correspondence should be addressed.
Determination of minimum sample size and discriminatory expression
patterns in microarray data
Authors: Hwang Daehee , Schmitt William A. , Stephanopoulos George , Stephanopoulos Gregory 
Published: September 2002

Abstract
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
* To whom correspondence should be addressed.
Bayesian infinite mixture model based clustering of gene expression profiles
Authors: Medvedovic Mario , Sivaganesan Siva 
Published: September 2002

Abstract
Motivation: The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data.
Results: We developed a clustering procedure based on the Bayesian infinite mixture model and applied it to clustering gene expression profiles. Clusters of genes with similar expression patterns are identified from the posterior distribution of clusterings defined implicitly by the stochastic data-generation model. The posterior distribution of clusterings is estimated by a Gibbs sampler. We summarized the posterior distribution of clusterings by calculating posterior pairwise probabilities of co-expression and used the complete linkage principle to create clusters. This approach has several advantages over usual clustering procedures. The analysis allows for incorporation of a reasonable probabilistic model for generating data. The method does not require specifying the number of clusters and resulting optimal clustering is obtained by averaging over models with all possible numbers of clusters. Expression profiles that are not similar to any other profile are automatically detected, the method incorporates experimental replicates, and it can be extended to accommodate missing data. This approach represents a qualitative shift in the model-based cluster analysis of expression data because it allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles. We also demonstrated the importance of incorporating the information on experimental variability into the clustering model.
Availability: The MS Windows™ based program implementing the Gibbs sampler and supplemental material is available at http://homepages.uc.edu/~medvedm/BioinformaticsSupplement.htm
Contact: medvedm@email.uc.edu
* To whom correspondence should be addressed.
Ratio statistics of gene expression levels and applications to
microarray data analysis
Authors: Chen Yidong , Kamat Vishnu , Dougherty Edward R. , Bittner Michael L. , Meltzer Paul S. , Trent Jeffery M. 
Published: September 2002

Abstract
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
* To whom correspondence should be addressed.
Multi-class cancer classification via partial least
squares with gene expression profiles
Authors: Nguyen Danh V. , Rocke David M. 
Published: September 2002

Abstract
Motivation: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously.
Results: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39–50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39–50).
Contact: dnguyen@stat.tamu.edudmrocke@ucdavis.edu
Optimal enhancement of immune response
Authors: Stengel Robert F. , Ghigliazza Raffaele M. , Kulkarni Nilesh V. 
Published: September 2002

Abstract
Motivation: Therapeutic enhancement of innate immune response to microbial attack is addressed as the optimal control of a dynamic system. Interactions between an invading pathogen and the innate immune system are characterized by four non-linear, ordinary differential equations that describe rates of change of pathogen, plasma cell, and antibody concentrations, and of an indicator of organic health. Without therapy, the dynamic model evidences sub-clinical or clinical decay, chronic stabilization, or unrestrained lethal growth of the pathogen; the response pattern depends on the initial concentration of pathogens in the simulated attack. In the model, immune response can be augmented by therapeutic agents that kill the pathogen directly, that stimulate the production of plasma cells or antibodies, or that enhance organ health. A previous paper demonstrated open-loop optimal control solutions that defeat the pathogen and preserve organ health, given initial conditions that otherwise would be lethal (Stengel et al. (2002)). Therapies based on separate and combined application of the agents were derived by minimizing a quadratic cost function that weighted both system response and control usage, providing implicit control over harmful side effects.
Results: We demonstrate the ability of neighboring–optimal feedback control to account for a range of unknown initial conditions and persistent input of pathogens by adjusting the therapy to account for perturbations from the nominal-optimal response history. We examine therapies that combine open-loop control of one agent with closed-loop control of another. We show that optimal control theory points the way toward new protocols for treatment and cure of human diseases.
Contact: stengel@princeton.edurghiglia@princeton.edunkulkarn@princeton.edu
* To whom correspondence should be addressed.
The correlation error and finite-size correction in an ungapped
sequence alignment
Authors: Park Yonil , Spouge John L. 
Published: September 2002

Abstract
Motivation: The BLAST program for comparing two sequences assumes independent sequences in its random model. The resulting random alignment matrices have correlations across their diagonals. Analytic formulas for the BLAST p-value essentially neglect these correlations and are equivalent to a random model with independent diagonals. Progress on the independent diagonals model has been surprisingly rapid, but the practical magnitude of the correlations it neglects remains unknown. In addition, BLAST uses a finite-size correction that is particularly important when either of the sequences being compared is short. Several formulas for the finite-size correction have now been given, but the corresponding errors in the BLAST p-values have not been quantified. As the lengths of compared sequences tend to infinity, it is also theoretically unknown whether the neglected correlations vanish faster than the finite-size correction.
Results: Because we required certain analytic formulas, our study restricted its computer experiments to ungapped sequence alignment. We expect some of our conclusions to extend qualitatively to gapped sequence alignment, however. With this caveat, the finite-size correction appeared to vanish faster than the neglected correlations. Although the finite-size correction underestimated the BLAST p-value, it improved the approximation substantially for all but very short sequences. In practice, the Altschul–Gish finite-size correction was superior to Spouge's. The independent diagonals model was always within a factor of 2 of the true BLAST p-value, although fitting p-value parameters from it probably is unwise.
Contact: spouge@ncbi.nlm.nih.gov
* To whom correspondence should be addressed.
The use of structure information to increase alignment
accuracy does not aid homologue detection with profile HMMs
Authors: Griffiths-Jones Sam , Bateman Alex 
Published: September 2002

Abstract
Motivation: The best quality multiple sequence alignments are generally considered to derive from structural superposition. However, no previous work has studied the relative performance of profile hidden Markov models (HMMs) derived from such alignments. Therefore several alignment methods have been used to generate multiple sequence alignments from 348 structurally aligned families in the HOMSTRAD database. The performance of profile HMMs derived from the structural and sequence-based alignments has been assessed for homologue detection.
Results: The best alignment methods studied here correctly align nearly 80% of residues with respect to structure alignments. Alignment quality and model sensitivity are found to be dependent on average number, length, and identity of sequences in the alignment. The striking conclusion is that, although structural data may improve the quality of multiple sequence alignments, this does not add to the ability of the derived profile HMMs to find sequence homologues.
Supplementary information: A list of HOMSTRAD families used in this study and the corresponding Pfam families is available at http://www.sanger.ac.uk/Users/sgj/alignments/map.html
Contact: sgj@sanger.ac.uk
* To whom correspondence should be addressed.
ESyPred3D: Prediction of proteins 3D structures
Authors: Lambert Christophe , Léonard Nadia , De Bolle Xavier , Depiereux Eric 
Published: September 2002

Abstract
Motivation: Homology or comparative modeling is currently the most accurate method to predict the three-dimensional structure of proteins. It generally consists in four steps: (1) databanks searching to identify the structural homolog, (2) target–template alignment, (3) model building and optimization, and (4) model evaluation. The target–template alignment step is generally accepted as the most critical step in homology modeling.
Results: We present here ESyPred3D, a new automated homology modeling program. The method gets benefit of the increased alignment performances of a new alignment strategy. Alignments are obtained by combining, weighting and screening the results of several multiple alignment programs. The final three-dimensional structure is build using the modeling package MODELLER. ESyPred3D was tested on 13 targets in the CASP4 experiment (Critical Assessment of Techniques for Proteins Structural Prediction). Our alignment strategy obtains better results compared to PSI-BLAST alignments and ESyPred3D alignments are among the most accurate compared to those of participants having used the same template.
Availability: ESyPred3D is available through its web site at http://www.fundp.ac.be/urbm/bioinfo/esypred/
Contact: christophe.lambert@fundp.ac.behttp://www.fundp.ac.be/~lambertc
* To whom correspondence should be addressed.