bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quick calculation for sample size while controlling false discovery rate with application to microarray analysis
Authors: Liu Peng , Hwang J. T. Gene 
Published: January 2007

AbstractMotivation: Sample size calculation is important in experimental design and is even more so in microarray or proteomic experiments since only a few repetitions can be afforded. In the multiple testing problems involving these experiments, it is more powerful and more reasonable to control false discovery rate (FDR) or positive FDR (pFDR) instead of type I error, e.g. family-wise error rate (FWER). When controlling FDR, the traditional approach of estimating sample size by controlling type I error is no longer applicable.Results: Our proposed method applies to controlling FDR. The sample size calculation is straightforward and requires minimal computation, as illustrated with two sample t-tests and F-tests. Based on simulation with the resultant sample size, the power is shown to be achievable by the q-value procedure.Availability: A Matlab code implementing the described methods is available upon request.Contact: pliu@iastate.eduSupplementary information: Supplementary data are available at Bioinformatics online.
An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences
Authors: Ye Kai , Kosters Walter A. , IJzerman Adriaan P. 
Published: January 2007

AbstractMotivation: Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In contrast, two algorithms, PRATT2 (http//www.ebi.ac.uk/pratt/) and TEIRESIAS (http://cbcsrv.watson.ibm.com/) are used to directly identify frequent patterns from unaligned biological sequences without an attempt to align them. Here we propose a new algorithm with more efficiency and more functionality than both PRATT2 and TEIRESIAS, and discuss some of its applications to G protein-coupled receptors, a protein family of important drug targets.Results: In this study, we designed and implemented six algorithms to mine three different pattern types from either one or two datasets using a pattern growth approach. We compared our approach to PRATT2 and TEIRESIAS in efficiency, completeness and the diversity of pattern types. Compared to PRATT2, our approach is faster, capable of processing large datasets and able to identify the so-called type III patterns. Our approach is comparable to TEIRESIAS in the discovery of the so-called type I patterns but has additional functionality such as mining the so-called type II and type III patterns and finding discriminating patterns between two datasets.Availability: The source code for pattern growth algorithms and their pseudo-code are available at http://www.liacs.nl/home/kosters/pg/Contact: k.ye@lacdr.leidenuniv.nl
A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors
Authors: Yoneya Takashi , Mamitsuka Hiroshi 
Published: January 2007

AbstractMotivation: Time series experiments of cDNA microarrays have been commonly used in various biological studies and conducted under a lot of experimental factors. A popular approach of time series microarray analysis is to compare one gene with another in their expression profiles, and clustering expression sequences is a typical example. On the other hand, a practically important issue in gene expression is to identify the general timing difference that is caused by experimental factors. This type of difference can be extracted by comparing a set of time series expression profiles under a factor with those under another factor, and so it would be difficult to tackle this issue by using only a current approach for time series microarray analysis.Results: We have developed a systematic method to capture the timing difference in gene expression under different experimental factors, based on hidden Markov models. Our model outputs a real-valued vector at each state and has a unique state transition diagram. The parameters of our model are trained from a given set of pairwise (generally multiplewise) expression sequences. We evaluated our model using synthetic as well as real microarray datasets. The results of our experiment indicate that our method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress.Contact: t-yoneya@kirin.co.jpSupplementary information: Supplementary data are available at Bioinformatics online.
SBaddon: high performance simulation for the Systems Biology Toolbox for MATLAB
Authors: Schmidt Henning 
Published: January 2007

AbstractSummary: We present the SBaddon package as an extension to the Systems Biology Toolbox for MATLAB (SBtoolbox). The goal of this extension is to provide the users of the SBtoolbox with important functionality that is needed for parameter estimation applications. While simulation in the SBtoolbox relies on the MATLAB ODE solvers, the SBaddon package provides considerably increased simulation performance through automatic generation of compiled simulation functions. Furthermore, the package contains improved optimization algorithms, forward parameter sensitivity analysis and basic numeric parameter identifiability analysis.Availability: The SBaddon package is open source and freely available for non-commercial use. Commercial use of SBaddon is only possible through a specific licensing agreement (contact sbaddon@sbtoolbox.org). SBaddon can be obtained from http://www.sbtoolbox.org/SBaddon. The website also contains extensive documentation, and examples.Contact: henning@fcc.chalmers.se
A 3D pattern matching algorithm for DNA sequences
Authors: Hérisson Joan , Payen Guillaume , Gherbi Rachid 
Published: January 2007

AbstractMotivation: Biologists usually work with textual DNA sequences (succession of A, C, G and T). This representation allows biologists to study the syntax and other linguistic properties of DNA sequences. Nevertheless, such a linear coding offers only a local and a one-dimensional vision of the molecule. The 3D structure of DNA is known to be very important in many essential biological mechanisms. By using 3D conformation models, one is able to construct a 3D trajectory of a naked DNA molecule. From the various studies that we performed, it turned out that two very different textual DNA sequences could have similar 3D structures.Results: In this article, we address a new research work on 3D pattern matching for DNA sequences. The aim of this work is to enhance conventional pattern matching analyses with 3D-augmented criteria. We have developed an algorithm, based on 3D trajectories, which compares angles formed by these trajectories and thus quantifies the difference between two 3D DNA sequences. This analysis performs from a global scale to al local one.Availability: Available on request from the authors.Contact: herisson@epigenomique.genopole.fr
Analysis ofE.colipromoter recognition problem in dinucleotide feature space
Authors: Rani T. Sobha , Bhavani S. Durga , Bapi Raju S. 
Published: January 2007

AbstractMotivation: Patterns in the promoter sequences within a species are known to be conserved but there exist many exceptions to this rule which makes the promoter recognition a complex problem. Although many complex feature extraction schemes coupled with several classifiers have been proposed for promoter recognition in the current literature, the problem is still open.Results: A dinucleotide global feature extraction method is proposed for the recognition of sigma-70 promoters in Escherichia coli in this article. The positive data set consists of sigma-70 promoters with known transcription starting points which are part of regulonDB and promec databases. Four different kinds of negative data sets are considered, two of them biological sets (Gordon et al., 2003) and the other two synthetic data sets. Our results reveal that a single-layer perceptron using dinucleotide features is able to achieve an accuracy of 80% against a background of biological non-promoters and 96% for random data sets. A scheme for locating the promoter regions in a given genome sequence is proposed. A deeper analysis of the data set shows that there is a bifurcation of the data set into two distinct classes, a majority class and a minority class. Our results point out that majority class constituting the majority promoter and the majority non-promoter signal is linearly separable. Also the minority class is linearly separable. We further show that the feature extraction and classification methods proposed in the paper are generic enough to be applied to the more complex problem of eucaryotic promoter recognition. We present Drosophila promoter recognition as a case study.Availability: http://202.41.85.117/htmfiles/faculty/tsr/tsr.htmlContact: tsrcs@uohyd.ernet.in
BioWeka—extending the Weka framework for bioinformatics
Authors: Gewehr Jan E. , Szugat Martin , Zimmer Ralf 
Published: January 2007

AbstractSummary: Given the growing amount of biological data, data mining methods have become an integral part of bioinformatics research. Unfortunately, standard data mining tools are often not sufficiently equipped for handling raw data such as e.g. amino acid sequences. One popular and freely available framework that contains many well-known data mining algorithms is the Waikato Environment for Knowledge Analysis (Weka). In the BioWeka project, we introduce various input formats for bioinformatics data and bioinformatics methods like alignments to Weka. This allows users to easily combine them with Weka's classification, clustering, validation and visualization facilities on a single platform and therefore reduces the overhead of converting data between different data formats as well as the need to write custom evaluation procedures that can deal with many different programs. We encourage users to participate in this project by adding their own components and data formats to BioWeka.Availability: The software, documentation and tutorial are available at http://www.bioweka.org.Contact: support@bioweka.org
DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins
Authors: Hwang Seungwoo , Gou Zhenkun , Kuznetsov Igor B. 
Published: January 2007

AbstractSummary: This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence.Availability: Freely available at http://lcg.rit.albany.edu/dp-bindContact: IKuznetsov@albany.eduSupplementry information: http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html
Identifying clusters of functionally related genes in genomes
Authors: Yi Gangman , Sze Sing-Hoi , Thon Michael R. 
Published: January 2007

AbstractMotivation: An increasing body of literature shows that genomes of eukaryotes can contain clusters of functionally related genes. Most approaches to identify gene clusters utilize microarray data or metabolic pathway databases to find groups of genes on chromosomes that are linked by common attributes. A generalized method that can find gene clusters regardless of the mechanism of origin would provide researchers with an unbiased method for finding clusters and studying the evolutionary forces that give rise to them.Results: We present an algorithm to identify gene clusters in eukaryotic genomes that utilizes functional categories defined in graph-based vocabularies such as the Gene Ontology (GO). Clusters identified in this manner need only have a common function and are not constrained by gene expression or other properties. We tested the algorithm by analyzing genomes of a representative set of species. We identified species-specific variation in percentage of clustered genes as well as in properties of gene clusters including size distribution and functional annotation. These properties may be diagnostic of the evolutionary forces that lead to the formation of gene clusters.Availability: A software implementation of the algorithm and example output files are available at http://fcg.tamu.edu/C_Hunter/.Contact: mthon@tamu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Quaternionic periodicity transform: an algebraic solution to the tandem repeat detection problem
Authors: Brodzik Andrzej K. 
Published: January 2007

AbstractMotivation: One of the main tasks of DNA sequence analysis is identification of repetitive patterns. DNA symbol repetitions play a key role in a number of applications, including prediction of gene and exon locations, identification of diseases, reconstruction of human evolutionary history and DNA forensics.Results: A new approach towards identification of tandem repeats in DNA sequences is proposed. The approach is a refinement of previously considered method, based on the complex periodicity transform. The refinement is obtained, among others, by mapping of DNA symbols to pure quaternions. This mapping results in an enhanced, symbol-balanced sensitivity of the transform to DNA patterns, and an unambiguous threshold selection criterion. Computational efficiency of the transform is further improved, and coupling of the computation with the period value is removed, thereby facilitating parallel implementation of the algorithm. Additionally, a post-processing stage is inserted into the algorithm, enabling unambiguous display of results in a convenient graphical format. Comparison of the quaternionic periodicity transform with two well-known pattern detection techniques shows that the new approach is competitive with these two techniques in detection of exact and approximate repeats.Supplementary information: Supplementary data are available at Bioinformatics online.
The ESID Online Database network
Authors: Guzman D. , Veit D. , Knerr V. , Kindle G. , Gathmann B. , Eades-Perner A. M. , Grimbacher B. 
Published: January 2007

AbstractSummary: Primary immunodeficiencies (PIDs) belong to the group of rare diseases. The European Society for Immunodeficiencies (ESID), is establishing an innovative European patient and research database network for continuous long-term documentation of patients, in order to improve the diagnosis, classification, prognosis and therapy of PIDs. The ESID Online Database is a web-based system aimed at data storage, data entry, reporting and the import of pre-existing data sources in an enterprise business-to-business integration (B2B). The online database is based on Java 2 Enterprise System (J2EE) with high-standard security features, which comply with data protection laws and the demands of a modern research platform.Availability: The ESID Online Database is accessible via the official website (http://www.esid.org/).Contact: b.grimbacher@medsch.ucl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Inferring pairwise regulatory relationships from multiple time series datasets
Authors: Shi Yanxin , Mitchell Tom , Bar-Joseph Ziv 
Published: January 2007

AbstractMotivation: Time series expression experiments have emerged as a popular method for studying a wide range of biological systems under a variety of conditions. One advantage of such data is the ability to infer regulatory relationships using time lag analysis. However, such analysis in a single experiment may result in many false positives due to the small number of time points and the large number of genes. Extending these methods to simultaneously analyze several time series datasets is challenging since under different experimental conditions biological systems may behave faster or slower making it hard to rely on the actual duration of the experiment.Results: We present a new computational model and an associated algorithm to address the problem of inferring time-lagged regulatory relationships from multiple time series expression experiments with varying (unknown) time-scales. Our proposed algorithm uses a set of known interacting pairs to compute a temporal transformation between every two datasets. Using this temporal transformation we search for new interacting pairs. As we show, our method achieves a much lower false-positive rate compared to previous methods that use time series expression data for pairwise regulatory relationship discovery. Some of the new predictions made by our method can be verified using other high throughput data sources and functional annotation databases.Availability: Matlab implementation is available from the supporting website: http://www.cs.cmu.edu/~yanxins/regulation_inference/index.htmlContact: zivbj@cs.cmu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Improving the accuracy of transmembrane protein topology prediction using evolutionary information
Authors: Jones David T. 
Published: January 2007

AbstractMotivation: Many important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell–cell communication, cell recognition and cell adhesion are mediated by membrane proteins. Unfortunately, as these proteins are not water soluble, it is extremely hard to experimentally determine their structure. Therefore, improved methods for predicting the structure of these proteins are vital in biological research. In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm.Results: A new method (MEMSAT3) for predicting transmembrane protein topology from sequence profiles is described and benchmarked with full cross-validation on a standard data set of 184 transmembrane proteins. The method is found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set. This compares with accuracies of 62–72% for other popular methods on the same benchmark. By using a second neural network specifically to discriminate transmembrane from globular proteins, a very low overall false positive rate (0.5%) can also be achieved in detecting transmembrane proteins.Availability: An implementation of the described method is available both as a web server (http://www.psipred.net) and as downloadable source code from http://bioinf.cs.ucl.ac.uk/memsat. Both the server and source code files are free to non-commercial users. Benchmark and training data are also available from http://bioinf.cs.ucl.ac.uk/memsat.Contact: dtj@cs.ucl.ac.uk
Peak selection from MALDI-TOF mass spectra using ant colony optimization
Authors: Ressom H. W. , Varghese R. S. , Drake S. K. , Hortin G. L. , Abdel-Hamid M. , Loffredo C. A. , Goldman R. 
Published: January 2007

AbstractMotivation: Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks.Results: The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method.Availability: Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htmContact: hwr@georgetown.eduSupplementary information: Supplementary data are available at Bioinformatics online.
SMotif: a server for structural motifs in proteins
Authors: Pugalenthi Ganesan , Suganthan P. N. , Sowdhamini R. , Chakrabarti Saikat 
Published: January 2007

AbstractSummary: SMotif is a server that identifies important structural segments or motifs for a given protein structure(s) based on conservation of both sequential as well as important structural features such as solvent inaccessibility, secondary structural content, hydrogen bonding pattern and residue packing. This server also provides three-dimensional orientation patterns of the identified motifs in terms of inter-motif distances and torsion angles. These motifs may form the common core and therefore, can also be employed to design and rationalize protein engineering and folding experiments.Availability: SMotif server is available via the URL http://caps.ncbs.res.in/SMotif/index.html.Contact: chakraba@mail.nih.gov, mini@ncbs.res.in or EPNSugan@ntu.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online.
Automatic recognition and annotation of gene expression patterns of fly embryos
Authors: Zhou Jie , Peng Hanchuan 
Published: January 2007

AbstractMotivation: Gene expression patterns obtained by in situ mRNA hybridization provide important information about different genes during Drosophila embryogenesis. So far, annotations of these images are done by manually assigning a subset of anatomy ontology terms to an image. This time-consuming process depends heavily on the consistency of experts.Results: We develop a system to automatically annotate a fruitfly's embryonic tissue in which a gene has expression. We formulate the task as an image pattern recognition problem. For a new fly embryo image, our system answers two questions: (1) Which stage range does an image belong to? (2) Which annotations should be assigned to an image? We propose to identify the wavelet embryo features by multi-resolution 2D wavelet discrete transform, followed by min-redundancy max-relevance feature selection, which yields optimal distinguishing features for an annotation. We then construct a series of parallel bi-class predictors to solve the multi-objective annotation problem since each image may correspond to multiple annotations.Supplementary information: The complete annotation prediction results are available at: http://www.cs.niu.edu/~jzhou/papers/fruitfly and http://research.janelia.org/peng/proj/fly_embryo_annotation/. The datasets used in experiments will be available upon request to the correspondence author.Contact: jzhou@cs.niu.edu and pengh@janelia.hhmi.org