bluuebunny/crossref_metadata_2025_split huggingface dataset:
SPI: a tool for incorporating gene expression data into a four-dimensional database of Caenorhabditis elegans embryogenesis
Authors: Minakuchi Yohei , Ito Masahiro , Kohara Yuji 
Published: March 2004

Abstract
Motivation: A comprehensive gene expression database is essential for computer modeling and simulation of biological phenomena, including development. Development is a four-dimensional (4D; 3D structure and time course) phenomenon. We are constructing a 4D database of gene expression for the early embryogenesis of the nematode Caenorhabditis elegans. As a framework of the 4D database, we have constructed computer graphics (CG), into which we will incorporate the expression data of a number of genes at the subcellular level. However, the assignment of 3D distribution of gene products (protein, mRNA), of embryos at various developmental stages, is both difficult and tedious. We need to automate this process. For this purpose, we developed a new system, named SPI after superimposing fluorescent confocal microscopic data onto a CG framework.
Results: The scheme of this system comprises the following: (1) acquirement of serial sections (40 slices) of fluorescent confocal images of three colors (4′,6′-diamino-2-phenylindole (DAPI) for nuclei, indodicarbocyanine (Cy-3) for the internal marker, which is a germline-specific protein POS-1 and indocarbocyanine (Cy-5) for the gene product to be examined); (2) identification of several features of the stained embryos, such as contour, developmental stage and position of the internal marker; (3) selection of CG images of the corresponding stage for template matching; (4) superimposition of serial sections onto the CG; (5) assignment of the position of superimposed gene products. The Snakes algorithm identified the embryo contour. The detection accuracy of embryo contours was 92.1% when applied to 2- to 28-cell-stage embryos. The accuracy of the developmental stage prediction method was 81.2% for 2- to 8-cell-stage embryos. We manually judged only the later stage embryos because the accuracy for embryos at the later stages was unsatisfactory due to experimental noise effects. Finally, our system chose the optimal CG and performed the superposition and assignment of gene product distribution. We established an initial 4D gene expression database with 56 maternal gene products.
Availability: This system is available at http://anti.lab.nig.ac.jp/spi/ and http://anti.lab.nig.ac.jp/4ddb/
Confirmation of data mining based predictions of protein function
Authors: King Ross D. , Wise Paul H. , Clare Amanda 
Published: March 2004

Abstract
Motivation: A central problem in bioinformatics is the assignment of function to sequenced open reading frames (ORFs). The most common approach is based on inferred homology using a statistically based sequence similarity (SIM) method, e.g. PSI-BLAST. Alternative non-SIM based bioinformatic methods are becoming popular. One such method is Data Mining Prediction (DMP). This is based on combining evidence from amino-acid attributes, predicted structure and phylogenic patterns; and uses a combination of Inductive Logic Programming data mining, and decision trees to produce prediction rules for functional class. DMP predictions are more general than is possible using homology. In 2000/1, DMP was used to make public predictions of the function of 1309 Escherichia coli ORFs. Since then biological knowledge has advanced allowing us to test our predictions.
Results: We examined the updated (20.02.02) Riley group genome annotation, and examined the scientific literature for direct experimental derivations of ORF function. Both tests confirmed the DMP predictions. Accuracy varied between rules, and with the detail of prediction, but they were generally significantly better than random. For voting rules, accuracies of 75–100% were obtained. Twenty-one of these DMP predictions have been confirmed by direct experimentation. The DMP rules also have interesting biological explanations. DMP is, to the best of our knowledge, the first non-SIM based prediction method to have been tested directly on new data.
Availability: We have designed the ‘Genepredictions’ database for protein functional predictions. This is intended to act as an open repository for predictions for any organism and can be accessed at http://www.genepredictions.org
PDBSprotEC: a Web-accessible database linking PDB chains to EC numbers via SwissProt
Authors: Martin Andrew C.R. 
Published: March 2004

Abstract
Summary: A mapping between chains in the Protein Databank and Enzyme Classification numbers is invaluable for research into structure–function relationships. Mapping at the chain level is a non-trivial problem and we present an automatically updated Web-server, which provides this link in a queryable form and as a downloadable XML or flat file.
Availability: The query interface and downloadable files may be accessed at http://www.bioinf.org.uk/pdbsprotec/
Supplementary information: Further details of the methods used are available at http://www.bioinf.org.uk/pdbsprotec/methods/
Predicting rules on organization of cis-regulatory elements, taking the order of elements into account
Authors: Terai Goro , Takagi Toshihisa 
Published: March 2004

Abstract
Motivation: In eukaryotes, rules regarding organization of cis-regulatory elements are complex. They sometimes govern multiple kinds of elements and positional restrictions on elements.
Results: We propose a method for detecting rules, by which the order of elements is restricted. The order restriction is expressed as element patterns. We extract all the element patterns that occur in promoter regions of at least the specified number of genes. Then, we find significant patterns based on the expression similarity of genes with promoter regions containing each of the extracted patterns. When we applied our method to Saccharomyces cerevisiae, we detected significant patterns overlooked by previous methods, thus demonstrating the utility of our method for analyses of eukaryotic gene regulation. We also suggest that several types of element organization exist: (i) those in which only the order of elements is important, (ii) order and distance both are important and (iii) only the combination of elements is important.
Availability: The program for extracting element patterns is available upon request.
Supplementary information: http://elpa.hgc.jp/ElementPatterns
CompuCell, a multi-model framework for simulation of morphogenesis
Authors: Izaguirre J. A. , Chaturvedi R. , Huang C. , Cickovski T. , Coffland J. , Thomas G. , Forgacs G. , Alber M. , Hentschel G. , Newman S. A. , Glazier J. A. 
Published: March 2004

Abstract
Motivation: CompuCell is a multi-model software framework for simulation of the development of multicellular organisms known as morphogenesis. It models the interaction of the gene regulatory network with generic cellular mechanisms, such as cell adhesion, division, haptotaxis and chemotaxis. A combination of a state automaton with stochastic local rules and a set of differential equations, including subcellular ordinary differential equations and extracellular reaction–diffusion partial differential equations, model gene regulation. This automaton in turn controls the differentiation of the cells, and cell–cell and cell–extracellular matrix interactions that give rise to cell rearrangements and pattern formation, e.g. mesenchymal condensation. The cellular Potts model, a stochastic model that accurately reproduces cell movement and rearrangement, models cell dynamics. All these models couple in a controllable way, resulting in a powerful and flexible computational environment for morphogenesis, which allows for simultaneous incorporation of growth and spatial patterning.
Results: We use CompuCell to simulate the formation of the skeletal architecture in the avian limb bud.
Availability: Binaries and source code for Microsoft Windows, Linux and Solaris are available for download from http://sourceforge.net/projects/compucell/
AmpliBASE MT™: a Mycobacterium tuberculosis diversity knowledgebase
Authors: Majeed Ahmed A. , Ahmed Niyaz , Rao K. Rajender , Ghousunnissa S. , Kauser Farhana , Bose Buddhaditta , Nagarajaram H. A. , Katoch V. M. , Cousins Debby V. , Sechi Leonardo A. , Gilman Robert H. , Hasnain Seyed E. 
Published: March 2004

Abstract
Summary: AmpliBASE MT™ is an online databank of high-resolution DNA fingerprints representing fluorescent amplified fragment length polymorphism (FAFLP) profiles or amplitypes developed for the Mycobacterium tuberculosis complex strains from 48 different countries. AmpliBASE MT™ is based on a relational database management system that is hyperlinked to visualize genotyping results in the form of DNA fingerprint images for individual strains. A flexible search system based on systematic comparisons of fragment sizes in base pairs allows inter-laboratory comparison of FAFLP profiles. Besides this, the database also displays previously published data on IS6110 profiles, spoligotypes, MIRU-VNTRs and large sequence polymorphisms along with the FAFLP records that will give the overall comparisons. Being the first of its kind, AmpliBASE MT™ is expected to be a very helpful tool in strengthening the concept of ‘geographic genomics’ and will be very helpful to molecular epidemiologists and those interested in diagnostic development for tuberculosis.
Availability: http://210.212.212.4/index.html
ChromoViz: multimodal visualization of gene expression data onto chromosomes using scalable vector graphics
Authors: Kim Jihoon , Chung Hee-Joon , Park Chan Hee , Park Woong-Yang , Kim Ju Han 
Published: March 2004

Abstract
Summary: ChromoViz is an R package for the visualization of microarray gene expression data, cross-species and cross-platform comparisons, as well as non-expression genomic data obtained from public databases onto chromosomes. Chromosomal visualization format is proposed for the clear decoupling of the data layer from the procedure layer and the combined visualization of genomic data from heterogeneous data sources. Visualization with Javascript-enabled scalable vector graphics enables interactive visualization and navigation of data objects on the Web.
Availability: http://www.snubi.org/software/ChromoViz/
Large-scale co-evolution analysis of protein structural interlogues using the global protein structural interactome map (PSIMAP)
Authors: Kim Wan K. , Bolser Dan M. , Park Jong H. 
Published: March 2004

Abstract
Motivation: Interacting pairs of proteins should co-evolve to maintain functional and structural complementarity. Consequently, such a pair of protein families shows similarity between their phylogenetic trees. Although the tendency of co-evolution has been known for various ligand–receptor pairs, it has not been studied systematically in the widest possible scope. We investigated the degree of co-evolution for more than 900 family pairs in a global protein structural interactome map (PSIMAP—a map of all the structural domain–domain interactions in the PDB).
Results: There was significant correlation in 45% of the total SCOPs Family level pairs, rising to 78% in 454 reliable family interactions. Expectedly, the intra-molecular interactions between protein families showed stronger co-evolution than inter-molecular interactions. However, both types of interaction have a fundamentally similar pattern of co-evolution except for cases where different interfaces are involved. These results validate the use of co-evolution analysis with predictive methods such as PSIMAP to improve the accuracy of prediction based on ‘homologous interaction’. The tendency of co-evolution enabled a nearly 5-fold enrichment in the identification of true interactions among the potential interlogues in PSIMAP. The estimated sensitivity was 79.2%, and the specificity was 78.6%.
Availability: The results of co-evolution analysis are available online at http://www.biointeraction.org
Predicting subcellular localization of proteins in a hybridization space
Authors: Cai Yu-Dong , Chou Kuo-Chen 
Published: March 2004

Abstract
Motivation: The localization of a protein in a cell is closely correlated with its biological function. With the number of sequences entering into databanks rapidly increasing, the importance of developing a powerful high-throughput tool to determine protein subcellular location has become self-evident. In view of this, the Nearest Neighbour Algorithm was developed for predicting the protein subcellular location using the strategy of hybridizing the information derived from the recent development in gene ontology with that from the functional domain composition as well as the pseudo amino acid composition.
Results: As a showcase, the same plant and non-plant protein datasets as investigated by the previous investigators were used for demonstration. The overall success rate of the jackknife test for the plant protein dataset was 86%, and that for the non-plant protein dataset 91.2%. These are the highest success rates achieved so far for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach (particularly by incorporating the knowledge of gene ontology) may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology.
Availability: The software would be made available on sending a request to the authors.
Multiple sequence alignment in parallel on a workstation cluster
Authors: Ebedes Justin , Datta Amitava 
Published: March 2004

Abstract
Summary: Multiple sequence alignment is the NP-hard problem of aligning three or more DNA or amino acid sequences in an optimal way so as to match as many characters as possible from the set of sequences. The popular sequence alignment program ClustalW uses the classical method of approximating a sequence alignment, by first computing a distance matrix and then constructing a guide tree to show the evolutionary relationship of the sequences. We show that parallelizing the ClustalW algorithm can result in significant speedup. We used a cluster of workstations using C and message passing interface for our implementation. Experimental results show that speedup of over 5.5 on six processors is obtainable for most inputs.
Availability: The software is available upon request from the second author.
CLENCH: a program for calculating Cluster ENriCHment using the Gene Ontology
Authors: Shah N.H. , Fedoroff N.V. 
Published: March 2004

Abstract
Summary: Analysis of microarray data most often produces lists of genes with similar expression patterns, which are then subdivided into functional categories for biological interpretation. Such functional categorization is most commonly accomplished using Gene Ontology (GO) categories. Although there are several programs that identify and analyze functional categories for human, mouse and yeast genes, none of them accept Arabidopsis thaliana data. In order to address this need for A.thaliana community, we have developed a program that retrieves GO annotations for A.thaliana genes and performs functional category analysis for lists of genes selected by the user.
Availability: http://www.personal.psu.edu/nhs109/Clench
Cluster Analyzer for Transcription Sites (CATS): a C++-based program for identifying clustered transcription factor binding sites
Authors: Yu Han , Yoo Andrew S. , Greenwald Iva 
Published: March 2004

Abstract
Summary: We have developed a program, Cluster Analyzer for Transcription Sites (CATS), which identifies clusters of transcription factor binding sites in any genome sequence. The program searches for clusters of the consensus sequence for DNA binding within a window (length of DNA). The window size and the cluster size (number of consensus sequences within a given window) can be varied. CATS can be used for single or multiple transcription factors for which consensus sequences have been deduced based on biochemical and mutational analysis, or by comparative genomics. The use of CATS for clusters of different transcription factor binding sites may facilitate the identification of genes that are co-regulated in a cell type-specific or developmental stage-specific manner. CATS is simple to install and use on computers running any Windows NT-platforms.
Availability: http://www.healthsciences.columbia.edu/dept/greenwaldlab/links.html
Gene structure prediction from consensus spliced alignment of multiple ESTs matching the same genomic locus
Authors: Brendel Volker , Xing Liqun , Zhu Wei 
Published: March 2004

Abstract
Motivation: Accurate gene structure annotation is a challenging computational problem in genomics. The best results are achieved with spliced alignment of full-length cDNAs or multiple expressed sequence tags (ESTs) with sufficient overlap to cover the entire gene. For most species, cDNA and EST collections are far from comprehensive. We sought to overcome this bottleneck by exploring the possibility of using combined EST resources from fairly diverged species that still share a common gene space. Previous spliced alignment tools were found inadequate for this task because they rely on very high sequence similarity between the ESTs and the genomic DNA.
Results: We have developed a computer program, GeneSeqer, which is capable of aligning thousands of ESTs with a long genomic sequence in a reasonable amount of time. The algorithm is uniquely designed to tolerate a high percentage of mismatches and insertions or deletions in the EST relative to the genomic template. This feature allows use of non-cognate ESTs for gene structure prediction, including ESTs derived from duplicated genes and homologous genes from related species. The increased gene prediction sensitivity results in part from novel splice site prediction models that are also available as a stand-alone splice site prediction tool. We assessed GeneSeqer performance relative to a standard Arabidopsis thaliana gene set and demonstrate its utility for plant genome annotation. In particular, we propose that this method provides a timely tool for the annotation of the rice genome, using abundant ESTs from other cereals and plants.
Availability: The source code is available for download at http://bioinformatics.iastate.edu/bioinformatics2go/gs/download.html. Web servers for Arabidopsis and other plant species are accessible at http://www.plantgdb.org/cgi-bin/AtGeneSeqer.cgi and http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi, respectively. For non-plant species, use http://bioinformatics.iastate.edu/cgi-bin/gs.cgi. The splice site prediction tool (SplicePredictor) is distributed with the GeneSeqer code. A SplicePredictor web server is available at http://bioinformatics.iastate.edu/cgi-bin/sp.cgi
Supplementary information: http://www.plantgdb.org/AtGDB/prj/BXZ03B
An evaluation of new criteria for CpG islands in the human genome as gene markers
Authors: Wang Yong , Leung Frederick C.C. 
Published: March 2004

Abstract
Motivation: Recently, more stringent criteria for CpG islands have been introduced to exclude Alu repeats, thereby enabling a higher proportion of CpG islands associating with genes to be identified. Using these new criteria, several types of associations between CpG islands and genes were investigated to further establish the importance of CpG islands as gene markers.
Results: The CpG islands were searched by CpGIE, a java software program developed for CpG island identification. CpGIE was advanced in identification accuracy compared with other tools. According to our results, about 70% of the identified CpG islands were associating with the human genes and over half of them are in the promoters. Furthermore, the investigation of genes in the confirmed gene model showed that 56% of them had a CpG island overlapping the transcription start sites. In comparison, the new criteria were found capable of filtering a large fraction of Alu repeats that was identified as CpG islands by the generally accepted criteria within the genes, but very few CpG islands associating with the promoters were affected. The genes in the predicted gene model were not obviously associated with CpG islands, suggesting that CpG islands can be used to evaluate the accuracy of gene annotation.
Availability: http://bioinfo.hku.hk/cpgieintro
Recognizing names in biomedical texts: a machine learning approach
Authors: Zhou GuoDong , Zhang Jie , Su Jian , Shen Dan , Tan ChewLim 
Published: March 2004

Abstract
Motivation: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition.
Results: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern, such as prefix and suffix; (3) part-of-speech; (4) head noun trigger; (5) special verb trigger and (6) name alias feature. All the features are integrated effectively and efficiently through a hidden Markov model (HMM) and a HMM-based named entity recognizer. In addition, a k-Nearest Neighbor (k-NN) algorithm is proposed to resolve the data sparseness problem in our system. Finally, we present a pattern-based post-processing to automatically extract rules from the training data to deal with the cascaded entity name phenomenon. From our best knowledge, PowerBioNE is the first system which deals with the cascaded entity name phenomenon. Evaluation shows that our system achieves the F-measure of 66.6 and 62.2 on the 23 classes of GENIA V3.0 and V1.1, respectively. In particular, our system achieves the F-measure of 75.8 on the ‘protein’ class of GENIA V3.0. For comparison, our system outperforms the best published result by 7.8 on GENIA V1.1, without help of any dictionaries. It also shows that our HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated HMM, support vector machines, C4.5, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem. Moreover, evaluation on GENIA V3.0 shows that the post-processing for the cascaded entity name phenomenon improves the F-measure by 3.9. Finally, error analysis shows that about half of the errors are caused by the strict annotation scheme and the annotation inconsistency in the GENIA corpus. This suggests that our system achieves an acceptable F-measure of 83.6 on the 23 classes of GENIA V3.0 and in particular 86.2 on the ‘protein’ class, without help of any dictionaries. We think that a F-measure of 90 on the 23 classes of GENIA V3.0 and in particular 92 on the ‘protein’ class, can be achieved through refining of the annotation scheme in the GENIA corpus, such as flexible annotation scheme and annotation consistency, and inclusion of a reasonable biomedical dictionary.
Availability: A demo system is available at http://textmining.i2r.a-star.edu.sg/NLS/demo.htm. Technology license is available upon the bilateral agreement.