bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chain functions and scoring functions in genetic networks
Authors: Gat-Viks I. , Shamir R. 
Published: July 2003

Abstract
One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such ‘reverse engineering’ effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations.
We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks.
Contact: {iritg,rshamir}@post.tau.ac.il
*To whom correspondence should be addressed.
Widespread predicted nonsense-mediated mRNA
decay of alternatively-spliced transcripts of human
normal and disease genes
Authors: Green Richard E. , Lewis Benjamin P. , Hillman R. Tyler , Blanchette Marco , Lareau Liana F. , Garnett Aaron T. , Rio Donald C. , Brenner Steven E. 
Published: July 2003

Abstract
We have recently shown that a third of reliably-inferred alternative mRNA isoforms are candidates for nonsense-mediated mRNA decay (NMD), an mRNA surveillance system (Lewis et al., 2003; Proc. Natl Acad. Sci. USA, 100, 189–192). Rather than being translated to yield protein, these transcripts are expected to be degraded and may be subject to regulated unproductive splicing and translation (RUST). Our initial experimental studies are consistent with these predictions and suggest an unappreciated role for NMD in several human diseases.
Contact: brenner@compbio.berkeley.edu
*To whom correspondence should be addressed.
Detecting protein sequence conservation via metric embeddings
Authors: Halperin E. , Buhler J. , Karp R. , Krauthgamer R. , Westover B. 
Published: July 2003

Abstract
Motivation: Comparing two protein databases is a fundamental task in biosequence annotation. Given two databases, one must find all pairs of proteins that align with high score under a biologically meaningful substitution score matrix, such as a BLOSUM matrix (Henikoff and Henikoff, 1992). Distance-based approaches to this problem map each peptide in the database to a point in a metric space, such that peptides aligning with higher scores are mapped to closer points. Many techniques exist to discover close pairs of points in a metric space efficiently, but the challenge in applying this work to proteomic comparison is to find a distance mapping that accurately encodes all the distinctions among residue pairs made by a proteomic score matrix. Buhler (2002) proposed one such mapping but found that it led to a relatively inefficient algorithm for protein-protein comparison.
Results: This work proposes a new distance mapping for peptides under the BLOSUM matrices that permits more efficient similarity search. We first propose a new distance function on peptides derived from a given score matrix. We then show how to map peptides to bit vectors such that the distance between any two peptides is closely approximated by the Hamming distance (i.e. number of mismatches) between their corresponding bit vectors. We combine these two results with the lsh-all-pairs-sim algorithm of Buhler (2002) to produce an improved distance-based algorithm for proteomic comparison. An initial implementation of the improved algorithm exhibits sensitivity within 5% of that of the original lsh-all-pairs-sim, while running up to eight times faster.
Availability: The source of the code can be found at http://www.eecs.berkeley.edu/~eran/projects/embed
Contact: eran@eecs.berkeley.edu
Keywords: protein comparison, database indexing, metric embedding, Hamming space
*To whom correspondence should be addressed.
Sensitive pattern discovery with ‘fuzzy’ alignments of distantly
related proteins
Authors: Heger Andreas , Holm Liisa 
Published: July 2003

Abstract
Motivation: Evolutionary comparison leads to efficient functional characterisation of hypothetical proteins. Here, our goal is to map specific sequence patterns to putative functional classes. The evolutionary signal stands out most clearly in a maximally diverse set of homologues. This diversity, however, leads to a number of technical difficulties. The targeted patterns—as gleaned from structure comparisons—are too sparse for statistically significant signals of sequence similarity and accurate multiple sequence alignment.
Results: We address this problem by a fuzzy alignment model, which probabilistically assigns residues to structurally equivalent positions (attributes) of the proteins. We then apply multivariate analysis to the ‘attributes x proteins’ matrix. The dimensionality of the space is reduced using non-negative matrix factorization. The method is general, fully automatic and works without assumptions about pattern density, minimum support, explicit multiple alignments, phylogenetic trees, etc. We demonstrate the discovery of biologically meaningful patterns in an extremely diverse superfamily related to urease.
Contact: Liisa.Holm@Helsinki.fi
*To whom correspondence should be addressed.
Deriving phylogenetic trees from the similarity analysis of metabolic
pathways
Authors: Heymans Maureen , Singh Ambuj K. 
Published: July 2003

Abstract
Motivation: Comparative analysis of metabolic pathways in different genomes can give insights into the understanding of evolutionary and organizational relationships among species. This type of analysis allows one to measure the evolution of complete processes (with different functional roles) rather than the individual elements of a conventional analysis. We present a new technique for the phylogenetic analysis of metabolic pathways based on the topology of the underlying graphs. A distance measure between graphs is defined using the similarity between nodes of the graphs and the structural relationship between them. This distance measure is applied to the enzyme-enzyme relational graphs derived from metabolic pathways. Using this approach, pathways and group of pathways of different organisms are compared to each other and the resulting distance matrix is used to obtain a phylogenetic tree.
Results: We apply the method to the Citric Acid Cycle and the Glycolysis pathways of different groups of organisms, as well as to the Carbohydrate metabolic networks. Phylogenetic trees obtained from the experiments were close to existing phylogenies and revealed interesting relationships among organisms.
Availability: Software available upon request from the authors.
Supplementary information: The technical report is available at http://www.cs.ucsb.edu/~maureen/TR200233.pdf
Contact: ambuj@cs.ucsb.edu
Keywords: phylogenetic trees, metabolic pathways, graph comparison
*To whom correspondence should be addressed.
Using guide trees to construct multiple-sequence evolutionary HMMs
Authors: Holmes I. 
Published: July 2003

Abstract
Motivation: Score-based progressive alignment algorithms do dynamic programming on successive branches of a guide tree. The analogous probabilistic construct is an Evolutionary HMM. This is a multiple-sequence hidden Markov model (HMM) made by combining transducers (conditionally normalised Pair HMMs) on the branches of a phylogenetic tree.
Methods: We present general algorithms for constructing an Evolutionary HMM from any Pair HMM and for doing dynamic programming to any Multiple-sequence HMM.
Results: Our prototype implementation, Handel, is based on the Thorne-Kishino-Felsenstein evolutionary model and is benchmarked using structural reference alignments.
Availability: Handel can be downloaded under GPL from www.biowiki.org/Handel
Identification of functional clusters
of transcription factor binding motifs in genome
sequences: the MSCAN algorithm
Authors: Johansson Ö. , Alkema W. , Wasserman W. W. , Lagergren J. 
Published: July 2003

Abstract
Motivation:The identification of regulatory control regions within genomes is a major challenge. Studies have demonstrated that regulating regions can be described as locally dense clusters or modules of cis-acting transcription factor binding sites (TFBS). For well-described biological contexts, it is possible to train predictive algorithms to discern novel modules in genome sequences. However, utility of module detection methods has been severely limited by insufficient training data. For only a few tissues can one obtain sufficient numbers of literature-derived regulatory modules.
Results: We present a novel method, MSCAN, that circumvents the training data problem by measuring the statistical significance of any non-overlapping combination of TFBS in a window. Given a set of transcription factor binding profiles, a significance threshold, and a genomic sequence, MSCAN returns putative regulatory regions. We assess performance on two curated collections of regulatory regions; one each for tissue-specific expression in liver and skeletal muscle cells. The efficiency of MSCAN allows for predictive screens of entire genomes.
Availability: http://tfscan.cgb.ki.se/cgi-bin/MSCAN
Contact: wyeth@cmmt.ubc.ca
Keywords: transcription, gene networks, modules, motif, promoter.
*To whom correspondence should be addressed.
Complexity management in visualizing protein interaction networks
Authors: Ju Byong-Hyon , Han Kyungsook 
Published: July 2003

Abstract
Motivation: Protein-protein interaction networks often consist of thousands of nodes or more. This severely limits the utility of many graph drawing tools because they become too slow for an interactive analysis of the networks and because they produce cluttered drawings with many edge crossings.
Results: A new layout algorithm with complexity management operations in visualizing a large-scale protein interaction network was developed and implemented in a program called InterViewer3. InterViewer3 simplifies a complex network by collapsing a group of nodes with the same interacting partners into a composite node and by replacing a clique with a star-shaped subgraph. The experimental results demonstrated that InterViewer3 is one order of magnitude faster than the other drawing programs and that its complexity management is successful.
Availability: http://wilab.inha.ac.kr/protein/
Contact: khan@inha.ac.kr
Keywords: protein-protein interaction network, visualization, abstraction
*To whom correspondence should be addressed.
GENIA corpus—a semantically annotated corpus for bio-textmining
Authors: Kim J.-D. , Ohta T. , Tateisi Y. , Tsujii J. 
Published: July 2003

Abstract
Motivation: Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining.
Results: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400 000 words and almost 100 000 annotations for biological terms.
Availability: GENIA corpus is freely available at http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA
Keywords: Text Mining, Information Extraction, Corpus, Natural Language Processing, Computational Molecular Biology
*To whom correspondence should be addressed.
Predicting phenotype from patterns of annotation
Authors: King Oliver D. , Lee Jeffrey C. , Dudley Aimée M. , Janse Daniel M. , Church George M. , Roth Frederick P. 
Published: July 2003

Abstract
Motivation:Predicting the outcome of specific experiments (such as the growth of a particular mutant strain in a particular medium) has the potential to allow researchers to devote resources to experiments with higher expected numbers of ‘hits’.
Results: We use decision trees to predict phenotypes associated with Saccharomyces cerevisiae genes on the basis of Gene Ontology (GO) functional annotations from the Saccharomyces Genome Database (SGD) and other phenotypic annotations from the Yeast Phenotype Catalog at the Munich Information Center for Protein Sequences (MIPS). We assess the methodology in three ways: (1) we use cross-validation on the phenotypic annotations listed in MIPS, and show ROC curves indicating the tradeoff between true-positive rate and false-positive rate; (2) we do a literature-search for 100 of the predicted gene-phenotype associations that are not listed in MIPS, and find evidence for 43 of them; (3) we use deletion strains to experimentally assess 61 predicted gene-phenotype associations not listed in MIPS; significantly more of these deletion strains show abnormal growth than would be expected by chance.
Contact: fritz_roth@hms.harvard.edu
Supplementary Information: Complete results are available at http://llama.med.harvard.edu/~king/pheno.html
Keywords: decision trees; phenotype; gene function
*To whom correspondence should be addressed.
Detection and validation of single gene inversions
Authors: Lefebvre J.F. , El-Mabrouk N. , Tillier E. , Sankoff D. 
Published: July 2003

Abstract
Motivation: The biologically meaningful algorithmic study of genome rearrangement should take into account the distribution of sizes of the rearranged genomic fragments. In particular, it is important to know the prevalence of short inversions in order to understand the patterns of gene order disruption observed in comparative genomics.
Results: We find a large excess of short inversions, especially those involving a single gene, in comparison with a random inversion model. This is demonstrated through comparison of four pairs of bacterial genomes, using a specially-designed implementation of the Hannenhalli–Pevzner theory, and validated through experimentation on pairs of random genomes matched to the real pairs.
Availability: The main routines of the experimental software are available through consultation with the authors.
Contact: sankoff@uottawa.ca
Keywords: short inversions, reversals, genome rearrangement, genome evolution, comparative genomics, bacterial genomes, Hannenhalli--Pevzner algorithm, experimental algorithmics.
*To whom correspondence should be addressed.
Predicting protein function from protein/protein
interaction data: a probabilistic approach
Authors: Letovsky Stanley , Kasif Simon 
Published: July 2003

Abstract
Motivation:The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network.
Results: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae.
Availability: Source code available upon request. Results available athttp://genomics10.bu.edu/netmark
Contact: sletovsky@aol.com
Keywords: protein–protein interaction, protein function prediction, gene ontology, Markov Random fields
*To whom correspondence should be addressed.
An ENSEMBLE machine learning approach for
the prediction of all-alpha membrane proteins
Authors: Martelli Pier Luigi , Fariselli Piero , Casadio Rita 
Published: July 2003

Abstract
Motivation: All-alpha membrane proteins constitute a functionally relevant subset of the whole proteome. Their content ranges from about 10 to 30% of the cell proteins, based on sequence comparison and specific predictive methods. Due to the paucity of membrane proteins solved with atomic resolution, the training/testing sets of predictive methods for protein topography and topology routinely include very few well-solved structures mixed with a hundred proteins known with low resolution. Moreover, available predictors fail in predicting recently crystallised membrane proteins (Chen et al., 2002). Presently the number of well-solved membrane proteins comprises some 59 chains of low sequence homology. It is therefore possible to train/test predictors only with the set of proteins known with atomic resolution and evaluate more thoroughly the performance of different methods.
Results: We implement a cascade-neural network (NN), two different hidden Markov models (HMM), and their ensemble (ENSEMBLE) as a new method. We train and test in cross validation the three methods and ENSEMBLE on the 59 well resolved membrane proteins. ENSEMBLE scores with a per-protein accuracy of 90% for topography and 71% for topology, outperforming the best single method of 7 and 5 percentage points, respectively. When tested on a low resolution set of 151 proteins, with no homology with the 59 proteins, the per-protein accuracy of ENSEMBLE is 76% for topography and 68% for topology. Our results also indicate that the performance of ENSEMBLE is higher than that of the best predictors presently available on the Web.
Contact: gigi@biocomp.unibo.it; http://www.biocomp.unibo.it
*To whom correspondence should be addressed.
APDB: a novel measure for benchmarking sequence
alignment methods without reference alignments
Authors: O’Sullivan Orla , Zehnder Mark , Higgins Des , Bucher Philipp , Grosdidier Aurelien , Notredame Cédric 
Published: July 2003

Abstract
Motivation: We describe APDB, a novel measure for evaluating the quality of a protein sequence alignment, given two or more PDB structures. This evaluation does not require a reference alignment or a structure superposition. APDB is designed to efficiently and objectively benchmark multiple sequence alignment methods.
Results: Using existing collections of reference multiple sequence alignments and existing alignment methods, we show that APDB gives results that are consistent with those obtained using conventional evaluations. We also show that APDB is suitable for evaluating sequence alignments that are structurally equivalent. We conclude that APDB provides an alternative to more conventional methods used for benchmarking sequence alignment packages.
Availability: APDB is implemented in C, its source code and its documentation are available for free on request from the authors.
Contact: cedric.notredame@europe.com
*To whom correspondence should be addressed.
GeneLoc: exon-based integration of human genome maps
Authors: Rosen Naomi , Chalifa-Caspi Vered , Shmueli Orit , Adato Avital , Lapidot Michal , Stampnitzky Julie , Safran Marilyn , Lancet Doron 
Published: July 2003

Abstract
Motivation:Despite the numerous available whole-genome mapping resources, no comprehensive, integrated map of the human genome yet exists.
Results: GeneLoc, software adjunct to GeneCards and UDB, integrates gene lists by comparing genomic coordinates at the exon level and assigns unique and meaningful identifiers to each gene.
Availability: http://bioinfo.weizmann.ac.il/genecards and http://genecards.weizmann.ac.il/udb
Supplementary information: http://bioinfo.weizmann.ac.il/cards-bin/AboutGCids.cgi, http://genecards.weizmann.ac.il/GeneLocAlg.html
Contact: marilyn.safran@weizmann.ac.il
*To whom correspondence should be addressed.
The discovery net system for high throughput bioinformatics
Authors: Rowe Anthony , Kalaitzopoulos Dimitrios , Osmond Michelle , Ghanem Moustafa , Guo Yike 
Published: July 2003

Abstract
Motivation: Bioinformatics requires Grid technologies and protocols to build high performance applications without focusing on the low level detail of how the individual Grid components operate.
Results: The Discovery Net system is a middleware that allows service developers to integrate tools based on existing and emerging Grid standards such as web services. Once integrated, these tools can be used to compose reusable workflows using these services that can later be deployed as new services for others to use. Using the Discovery Net system and a range of different bioinformatics tools, we built a Grid based application for Genome Annotation. This includes workflows for automatic nucleotide annotation, annotation of predicted proteins and text analysis based on metabolic profiles and text analysis.
Contact: asr99@doc.ic.ac.uk
Keywords: grid, E-Science, annotation, workflow, pipeline.
*To whom correspondence should be addressed.
Pair hidden Markov models on tree structures
Authors: Sakakibara Yasubumi 
Published: July 2003

Abstract
Motivation: Computationally identifying non-coding RNA regions on the genome has much scope for investigation and is essentially harder than gene-finding problems for protein-coding regions. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignments of RNA sequences. On the other hand, Hidden Markov Models (HMMs) have played important roles for modeling and analysing biological sequences. Especially, the concept of Pair HMMs (PHMMs) have been examined extensively as mathematical models for alignments and gene finding.
Results: We propose the pair HMMs on tree structures (PHMMTSs), which is an extension of PHMMs defined on alignments of trees and provides a unifying framework and an automata-theoretic model for alignments of trees, structural alignments and pair stochastic context-free grammars. By structural alignment, we mean a pairwise alignment to align an unfolded RNA sequence into an RNA sequence of known secondary structure. First, we extend the notion of PHMMs defined on alignments of ‘linear’ sequences to pair stochastic tree automata, called PHMMTSs, defined on alignments of ‘trees’. The PHMMTSs provide various types of alignments of trees such as affine-gap alignments of trees and an automata-theoretic model for alignment of trees. Second, based on the observation that a secondary structure of RNA can be represented by a tree, we apply PHMMTSs to the problem of structural alignments of RNAs. We modify PHMMTSs so that it takes as input a pair of a ‘linear’ sequence and a ‘tree’ representing a secondary structure of RNA to produce a structural alignment. Further, the PHMMTSs with input of a pair of two linear sequences is mathematically equal to the pair stochastic context-free grammars. We demonstrate some computational experiments to show the effectiveness of our method for structural alignments, and discuss a complexity issue of PHMMTSs.
Contact: yasu@bio.keio.ac.jp
Keywords: hidden Markov model, tree automaton, structural alignment, stochastic context-free grammar, RNA, secondary structure.