bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative approach for computationally inferring
protein domain interactions
Authors: Ng See-Kiong , Zhang Zhuo , Tan Soon-Heng 
Published: May 2003

Abstract
Motivation: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions.
Results: Given that protein–protein interactions involve physical interactions between protein domains, domain–domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain–domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein–protein interactions.
Availability: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.
Contact: skng@lit.org.sg
* To whom correspondence should be addressed.
Fuzzy C-means method for clustering microarray data
Authors: Dembélé Doulaye , Kastner Philippe 
Published: May 2003

Abstract
Motivation: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes.
Results: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster.
Availability: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/
Contact: doulaye@titus.u-strasbg.fr
* To whom correspondence should be addressed.
Observing and interpreting correlations in metabolomic
networks
Authors: Steuer R. , Kurths J. , Fiehn O. , Weckwerth W. 
Published: May 2003

Abstract
Motivation: Metabolite profiling aims at an unbiased identification and quantification of all the metabolites present in a biological sample. Based on their pair-wise correlations, the data obtained from metabolomic experiments are organized into metabolic correlation networks and the key challenge is to deduce unknown pathways based on the observed correlations. However, the data generated is fundamentally different from traditional biological measurements and thus the analysis is often restricted to rather pragmatic approaches, such as data mining tools, to discriminate between different metabolic phenotypes.
Methods and Results: We investigate to what extent the data generated networks reflect the structure of the underlying biochemical pathways. The purpose of this work is 2-fold: Based on the theory of stochastic systems, we first introduce a framework which shows that the emergent correlations can be interpreted as a‘ fingerprint’ of the underlying biophysical system. This result leads to a systematic relationship between observed correlation networks and the underlying biochemical pathways. In a second step, we investigate to what extent our result is applicable to the problem of reverse engineering, i.e. to recover the underlying enzymatic reaction network from data. The implications of our findings for other bioinformatics approaches are discussed.
Contact: steuer@agnld.uni-potsdam.de
* To whom correspondence should be addressed.
ACGT—a comparative genomics tool
Authors: Xie Tao , Hood Leroy 
Published: May 2003

Abstract
Summary: ACGT (a comparative genomics tool) is a genomic DNA sequence comparison viewer and analyzer. It can read a pair of DNA sequences in GenBank, Embl or Fasta formats, with or without a comparison file, and provide users with many options to view and analyze the similarities between the input sequences. It is written in Java and can be run on Unix, Linux and Windows platforms.
Availability: The ACGT program is freely available with documentation and examples at website: http://db.systemsbiology.net/projects/local/mhc/acgt/
Contact: txie@systemsbiology.org
* To whom correspondence should be addressed.
Zerg: a very fast BLAST parser library
Authors: Paquola Apuã C.M. , Machado Abimael A. , Reis Eduardo M. , da Silva Aline M. , Verjovski-Almeida Sergio 
Published: May 2003

Abstract
Summary: Zerg is a library of sub-routines that parses the output from all NCBI BLAST programs (Blastn, Blastp, Blastx, Tblastn and Tblastx) and returns the attributes of a BLAST report to the user. It is optimized for speed, being especially useful for large-scale genomic analysis. Benchmark tests show that Zerg is over two orders of magnitude faster than some widely used BLAST parsers.
Availability: http://bioinfo.iq.usp.br/zerg
Contact: verjo@iq.usp.br
* To whom correspondence should be addressed.
Mining HIV dynamics using independent component analysis
Authors: Draghici Sorin , Graziano Frank , Kettoola Samira , Sethi Ishwar , Towfic George 
Published: May 2003

Abstract
Motivation: We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. We show that this technique takes advantage of the ICA power to eliminate the noise generated by artificial interaction of HIV system dynamics. Moreover, the incorporation of the actual laboratory data sets into the analysis phase offers a powerful advantage when compared with other mathematical procedures that consider the general behavior of HIV dynamics.
Results: The ICA algorithm has been used to generate different patterns of the HIV dynamics under different therapy conditions. The Kohonen Map has been used to eliminate redundant noise in each pattern to produce a reliable data set for the simulation phase. We show that under potent antiretroviral drugs, the value of the CD4+ cells in infected persons decreases gradually by about 11% every 100 days and the levels of the CD8+ cells increase gradually by about 2% every 100 days.
Availability: Executable code and data libraries are available by contacting the corresponding author.
Implementation: Mathematica 4 has been used to simulate the suggested model. A Pentium III or higher platform is recommended.
Contact: gtowfic@clarke.edu.
* To whom correspondence should be addressed.
3D-Jury: a simple approach to improve protein structure predictions
Authors: Ginalski Krzysztof , Elofsson Arne , Fischer Daniel , Rychlewski Leszek 
Published: May 2003

Abstract
Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins.
Results: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols.
Availability: The 3D-Jury system is available via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/) to the academic community.
Contact: leszek@bioinfo.pl
Supplementary information: 3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/)
* To whom correspondence should be addressed.
CAP: conformation angles package—displaying
the conformation angles of side chains in proteins
Authors: Sheik S. S. , Sundararajan P. , Shanthi V. , Sekar K. 
Published: May 2003

Abstract
Summary: A graphics package has been developed to display all side chain conformation angles of the user selected residue in a given protein structure. The proposed package is incorporated with all the protein structures (solved using X-ray diffraction and NMR spectroscopy) available in the Protein Data Bank. The package displays the multiple conformations adopted by a single amino acid residue whose structure is solved and refined at very high resolution. In addition, it shows the percentage distribution of the side chain conformation angles in different rotameric states.
Availability: http://144.16.71.146/cap/
Contact: sekar@physics.iisc.ernet.in
* To whom correspondence should be addressed.
Microarray standard data set and figures of merit
for comparing data processing methods and experiment designs
Authors: He Yudong D. , Dai Hongyue , Schadt Eric E. , Cavet Guy , Edwards Stephen W. , Stepaniants Sergey B. , Duenwald Sven , Kleinhanz Robert , Jones Allan R. , Shoemaker Daniel D. , Stoughton Roland B. 
Published: May 2003

Abstract
Motivation: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods.
Results: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the ∼ 24 000 60mer oligonucleotides that report ∼2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change.
Availability/Supplementary information: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm
Contact: yudong_he@merck.com
* To whom correspondence should be addressed.
† A wholly owned subsidiary of Merck & Co., Inc.
Computation method to identify differential allelic gene expression
and novel imprinted genes
Authors: Yang Howard H. , Hu Ying , Edmonson Michael , Buetow Kenneth , Lee Maxwell P. 
Published: May 2003

Abstract
Motivation: Genomic imprinting plays an important role in both normal development and diseases. Abnormal imprinting is strongly associated with several human diseases including cancers. Most of the imprinted genes were discovered in the neighborhood of the known imprinted genes. This approach is difficult to extend to analyze the whole genome. We have decided to take a computational approach to systematically search the whole genome for the presence of mono-allelic expressed genes and imprinted genes in human genome.
Results: A computational method was developed to identify novel imprinted or mono-allelic genes. Individuals represented in human cDNA libraries were genotyped using Bayesian statistics, and differential expression of polymorphic alleles was identified. A significant reduction in the number of libraries that expressed both alleles, measured by Z-statistics, is a strong indicator for an imprinted or a mono-allelic gene.
Availability: The data sets are available at http://leelab.nci.nih.gov/leelab/jsp/IGDM/IGDM.html
Contact: leemax@mail.nih.gov.
Supplementary information: http://leelab.nci.nih.gov/leelab/jsp/IGDM/IGDM.html
* To whom correspondence should be addressed.
Corrected small-sample estimation of the Bayes error
Authors: Brun Marcel , Sabbagh David , Kim Seungchan , Dougherty Edward R. 
Published: May 2003

Abstract
Motivation: A major problem of pattern classification is estimation of the Bayes error when only small samples are available. One way to estimate the Bayes error is to design a classifier based on some classification rule applied to sample data, estimate the error of the designed classifier, and then use this estimate as an estimate of the Bayes error. Relative to the Bayes error, the expected error of the designed classifier is biased high, and this bias can be severe with small samples.
Results: This paper provides a correction for the bias by subtracting a term derived from the representation of the estimation error. It does so for Boolean classifiers, these being defined on binary features. Although the general theory applies to any Boolean classifier, a model is introduced to reduce the number of parameters. A key point is that the expected correction is conservative. Properties of the corrected estimate are studied via simulation. The correction applies to binary predictors because they are mathematically identical to Boolean classifiers. In this context the correction is adapted to the coefficient of determination, which has been used to measure nonlinear multivariate relations between genes and design genetic regulatory networks. An application using gene-expression data from a microarray experiment is provided on the website http://gspsnap.tamu.edu/smallsample/ (user:‘smallsample’, password:‘smallsample)’).
Contact: edward@ee.tamu.edu
* To whom correspondence should be addressed.
Monitoring Socioeconomic Inequalities in Sexually Transmitted Infections, Tuberculosis, and Violence: Geocoding and Choice of Area-Based Socioeconomic Measures—The Public Health Disparities Geocoding Project (US)
Authors: Krieger Nancy , Waterman Pamela D. , Chen Jarvis T. , Soobader Mah-Jabeen , Subramanian SV 
Published: May 2003

Objectives.To determine which area-based socioeconomic measures, at which level of geography, are suitable for monitoring socioeconomic inequalities in sexually transmitted infections (STIs), tuberculosis (TB), and violence in the United States.Methods.Cross-sectional analysis of public health surveillance data, geocoded and linked to area-based socioeconomic measures generated from 1990 census tract, block group, and ZIP Code data. We included all incident cases among residents of either Massachusetts (MA; 1990 population = 6,016,425) or Rhode Island (RI; 1990 population = 1,003,464) for: STIs (MA: 1994–1998, n = 26,535 chlamydia, 7,464 gonorrhea, 2,619 syphilis; RI: 1994–1996, n = 4,473 chlamydia, 1,256 gonorrhea, 305 syphilis); TB (MA: 1993–1998, n = 1,793; RI: 1985–1994, n = 576), and non-fatal weapons related injuries (MA: 1995–1997, n = 6,628).Results.Analyses indicated that: ( a) block group and tract socioeconomic measures performed similarly within and across both states, with results more variable for the ZIP Code level measures; ( b) measures of economic deprivation consistently detected the steepest socioeconomic gradients, considered across all outcomes (incidence rate ratios on the order of 10 or higher for syphilis, gonorrhea, and non-fatal intentional weapons-related injuries, and 7 or higher for chlamydia and TB); and ( c) results were similar for categories generated by quintiles and by a priori categorical cut-points.Conclusions.Supplementing U.S. public health surveillance systems with census tract or block group area-based socioeconomic measures of economic deprivation could greatly enhance monitoring and analysis of social inequalities in health in the United States.
Fishing new proteins in the twilight zone of genomes: The test case of outer membrane proteins in Escherichia coli K12, Escherichia coli O157:H7, and other Gram‐negative bacteria
Authors: Casadio Rita , Fariselli Piero , Finocchiaro Giacomo , Martelli Pier Luigi 
Published: May 2003

AbstractWe address the problem of clustering the whole protein content of genomes into three different categories—globular, all‐α, and all‐β membrane proteins—with the aim of fishing new membrane proteins in the pool of nonannotated proteins (twilight zone). The focus is then mainly on outer membrane proteins. This is performed by using an integrated suite of programs (Hunter) specifically developed for predicting the occurrence of signal peptides in proteins of Gram‐negative bacteria and the topography of all‐α and all‐β membrane proteins. Hunter is tested on the well and partially annotated proteins (2160 and 760, respectively) of Escherichia coli K 12 scoring as high as 95.6% in the correct assignment of each chain to the category. Of the remaining 1253 nonannotated sequences, 1099 are predicted globular, 136 are all‐α, and 18 are all‐β membrane proteins. In Escherichia coli 0157:H7 we filtered 1901 nonannotated proteins. Our analysis classifies 1564 globular chains, 327 inner membrane proteins, and 10 outer membrane proteins. With Hunter, new membrane proteins are added to the list of putative membrane proteins of Gram‐negative bacteria. The content of outer membrane proteins per genome (nine are analyzed) ranges from 1.5% to 2.4%, and it is one order of magnitude lower than that of inner membrane proteins. The finding is particularly relevant when it is considered that this is the first large‐scale analysis based on validated tools that can predict the content of outer membrane proteins in a genome and can allow cross‐comparison of the same protein type between different species.
Reducing the computational complexity of protein folding via fragment folding and assembly
Authors: Haspel Nurit , Tsai Chung‐Jung , Wolfson Haim , Nussinov Ruth 
Published: May 2003

AbstractUnderstanding, and ultimately predicting, how a 1‐D protein chain reaches its native 3‐D fold has been one of the most challenging problems during the last few decades. Data increasingly indicate that protein folding is a hierarchical process. Hence, the question arises as to whether we can use the hierarchical concept to reduce the practically intractable computational times. For such a scheme to work, the first step is to cut the protein sequence into fragments that form local minima on the polypeptide chain. The conformations of such fragments in solution are likely to be similar to those when the fragments are embedded in the native fold, although alternate conformations may be favored during the mutual stabilization in the combinatorial assembly process. Two elements are needed for such cutting: (1) a library of (clustered) fragments derived from known protein structures and (2) an assignment algorithm that selects optimal combinations to “cover” the protein sequence. The next two steps in hierarchical folding schemes, not addressed here, are the combinatorial assembly of the fragments and finally, optimization of the obtained conformations. Here, we address the first step in a hierarchical protein‐folding scheme. The input is a target protein sequence and a library of fragments created by clustering building blocks that were generated by cutting all protein structures. The output is a set of cutout fragments. We briefly outline a graph theoretic algorithm that automatically assigns building blocks to the target sequence, and we describe a sample of the results we have obtained.
Analysis of transglutaminase protein substrates by functional proteomics
Authors: Ruoppolo Margherita , Orrù Stefania , D'Amato Alfonsina , Francese Simona , Rovero Paolo , Marino Gennaro , Esposito Carla 
Published: May 2003

AbstractTransglutaminases are calcium‐dependent enzymes that catalyze a post‐translational modification of proteins through the formation of ε‐(γ‐glutamyl)lysine bonds. Although specific roles for transglutaminases have been described, recent findings have provided evidence that dysregulation of transglutaminases may contribute to many pathological processes including celiac disease and neurodegenerative diseases. A crucial step in the elucidation of biological and pathological roles of transglutaminases requires the identification of protein substrates. A strategy based on a functional proteomic analysis was set up using two well‐characterized biotinylated transglutaminase substrates as affinity probes: 5–(biotinamido)pentylamine and the synthetic biotinylated peptide TVQQEL, the amino‐ and acyl‐donor probes, respectively. A pool of known tissue type transglutaminase protein substrates was selected in order to test the procedure. Results obtained in this paper indicate that the whole strategy can be successfully applied in order to identify transglutaminases protein substrates as well as the amino acid site sensitive toward enzyme activity.
Protein–protein docking with a reduced protein model accounting for side‐chain flexibility
Authors: Zacharias Martin 
Published: May 2003

AbstractA protein–protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue. Docking is performed by energy minimization in rotational and translational degrees of freedom. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico‐chemical character. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of ∼1 to 3 Å for the mobile protein partner from the experimental geometry. For most test cases, the energy‐minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. To account for side‐chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side‐chain conformation during the docking process. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within ∼2 to 3.5 Å RMSD of the full mobile partner from the experimental geometry was found among the 40 top‐scoring complexes. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures.