bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved network-based identification of protein orthologs
Authors: Yosef Nir , Sharan Roded , Noble William Stafford 
Published: August 2008

Abstract
Motivation: Identifying protein orthologs is an important task that is receiving growing attention in the bioinformatics literature. Orthology detection provides a fundamental tool towards understanding protein evolution, predicting protein functions and interactions, aligning protein–protein interaction (PPI) networks of different species and detecting conserved modules within these networks.
Results: Here, we present a novel diffusion-based framework that builds on the Rankprop algorithm for protein orthology detection and enhances it in several important ways. Specifically, we enhance the Rankprop algorithm to account for the presence of multiple paralogs, utilize PPI, and consider multiple (&gt;2) species in parallel. We comprehensively benchmarked our framework using a variety of training datasets and experimental settings. The results, based on the yeast, fly and human proteomes, show that the novel enhancements of Rankprop provide substantial improvements over its original formulation as well as over a number of state of the art methods for network-based orthology detection.
Availability: datasets and source code are available upon request.
Contact: niryosef@post.tau.ac.il
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities
Authors: Gao Pei , Honkela Antti , Rattray Magnus , Lawrence Neil D. 
Published: August 2008

AbstractMotivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalization of these latent chemical species through Gaussian process priors.Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarsegrained discretization of continuous time functions, which would lead to a large number of additional parameters to be estimated. We develop exact (for linear regulation) and approximate (for non-linear regulation) inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from http://www.cs.man.ac.uk/~neill/gpsim.Contact: neill@cs.man.ac.uk
Clinically driven semi-supervised class discovery in gene expression data
Authors: Steinfeld Israel , Navon Roy , Ardigò Diego , Zavaroni Ivana , Yakhini Zohar 
Published: August 2008

AbstractMotivation: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries.Results: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors.Availability: MonoClaD (Monotone Class Discovery). See http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/Supplementary information: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software.htmlContact: zohar_yakhini@agilent.com
ChIPmix: mixture model of regressions for two-color ChIP–chip analysis
Authors: Martin-Magniette Marie-Laure , Mary-Huard Tristan , Bérard Caroline , Robin Stéphane 
Published: August 2008

Abstract
Motivation: Chromatin immunoprecipitation (ChIP) combined with DNA microarray is a high-throughput technology to investigate DNA–protein binding or chromatin/histone modifications. ChIP–chip data require adapted statistical method in order to identify enriched regions. All methods already proposed are based on the analysis of the log ratio (Ip/Input). Nevertheless, the assumption that the log ratio is a pertinent quantity to assess the probe status is not always verified and it leads to a poor data interpretation.
Results: Instead of working on the log ratio, we directly work with the Ip and Input signals of each probe by modeling the distribution of the Ip signal conditional to the Input signal. We propose a method named ChIPmix based on a linear regression mixture model to identify actual binding targets of the protein under study. Moreover, we are able to control the proportion of false positives. The efficiency of ChIPmix is illustrated on several datasets obtained from different organisms and hybridized either on tiling or promoter arrays. This validation shows that ChIPmix is convenient for any two-color array whatever its density and provides promising results.
Availability: The ChIPmix method is implemented in R and is available at http://www.agroparistech.fr/mia/outil_A.html
Contact: marie_laure.martin@agroparistech.fr
Segment-based multiple sequence alignment
Authors: Rausch Tobias , Emde Anne-Katrin , Weese David , Döring Andreas , Notredame Cedric , Reinert Knut 
Published: August 2008

Abstract
Motivation: Many multiple sequence alignment tools have been developed in the past, progressing either in speed or alignment accuracy. Given the importance and wide-spread use of alignment tools, progress in both categories is a contribution to the community and has driven research in the field so far.
Results: We introduce a graph-based extension to the consistency-based, progressive alignment strategy. We apply the consistency notion to segments instead of single characters. The main problem we solve in this context is to define segments of the sequences in such a way that a graph-based alignment is possible. We implemented the algorithm using the SeqAn library and report results on amino acid and DNA sequences. The benefit of our approach is threefold: (1) sequences with conserved blocks can be rapidly aligned, (2) the implementation is conceptually easy, generic and fast and (3) the consistency idea can be extended to align multiple genomic sequences.
Availability: The segment-based multiple sequence alignment tool can be downloaded from http://www.seqan.de/projects/msa.html. A novel version of T-Coffee interfaced with the tool is available from http://www.tcoffee.org. The usage of the tool is described in both documentations.
Contact: rausch@inf.fu-berlin.de
Efficient representation and P-value computation for high-order Markov motifs
Authors: da Fonseca Paulo G. S. , Guimarães Katia S. , Sagot Marie-France 
Published: August 2008

Abstract
Motivation: Position weight matrices (PWMs) have become a standard for representing biological sequence motifs. Their relative simplicity has favoured the development of efficient algorithms for diverse tasks such as motif identification, sequence scanning and statistical significance evaluation. Markov chainbased models generalize the PWM model by allowing for interposition dependencies to be considered, at the cost of substantial computational overhead, which may limit their application.
Results: In this article, we consider two aspects regarding the use of higher order Markov models for biological sequence motifs, namely, the representation and the computation of P-values for motifs described by a set of occurrences. We propose an efficient representation based on the use of tries, from which empirical position-specific conditional base probabilities can be computed, and extend state-of-the-art PWM-based algorithms to allow for the computation of exact P-values for high-order Markov motif models.
Availability: The software is available in the form of a Java objectoriented library from http://www.cin.ufpe.br/~paguso/kmarkov.
Contact: paguso@cin.ufpe.br
From minimal signed circuits to the dynamics of Boolean regulatory networks
Authors: Remy Elisabeth , Ruet Paul 
Published: August 2008

AbstractIt is acknowledged that the presence of positive or negative circuits in regulatory networks such as genetic networks is linked to the emergence of significant dynamical properties such as multistability (involved in differentiation) and periodic oscillations (involved in homeostasis). Rules proposed by the biologist R. Thomas assert that these circuits are necessary for such dynamical properties. These rules have been studied by several authors. Their obvious interest is that they relate the rather simple information contained in the structure of the network (signed circuits) to its much more complex dynamical behaviour. We prove in this article a nontrivial converse of these rules, namely that certain positive or negative circuits in a regulatory graph are actually sufficient for the observation of a restricted form of the corresponding dynamical property, differentiation or homeostasis. More precisely, the crucial property that we require is that the circuit be globally minimal. We then apply these results to the vertebrate immune system, and show that the two minimal functional positive circuits of the model indeed behave as modules which combine to explain the presence of the three stable states corresponding to the Th0, Th1 and Th2 cells.Contact: ruet@iml.univ-mrs.fr
RNA structure alignment by a unit-vector approach
Authors: Capriotti Emidio , Marti-Renom Marc A. 
Published: August 2008

Abstract
Motivation: The recent discovery of tiny RNA molecules such as µRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance.
Results: The SARA program has been implemented to be of general applicability even when no secondary structure can be calculated from the RNA structures. A benchmark against the ARTS program using a set of 1275 non-redundant pairwise structure alignments results in ¡«6% extra alignments with at least 50% structurally superposed nucleotides and base pairs. A first attempt to perform RNA automatic functional annotation based on structure alignments indicates that SARA can correctly assign the deepest SCOR classification to &gt;60% of the query structures.
Availability: The SARA program is freely available through a World Wide Web server http://sgu.bioinfo.cipf.es/services/SARA/
Contact: mmarti@cipf.es
An integrative approach for predicting interactions of protein regions
Authors: Schelhorn Sven-Eric , Lengauer Thomas , Albrecht Mario 
Published: August 2008

AbstractMotivation: Protein–protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identification of interacting protein regions aids in the detailed understanding of protein networks and supports the prediction of novel protein interactions and the reconstruction of protein complexes.Results: We introduce an integrative approach for predicting protein region interactions using a probabilistic model fitted to an observed protein network. In particular, we consider globular domains, short linear motifs and coiled-coil regions as potential protein-binding regions. Possible cooperations between multiple regions within the same protein are taken into account. A finegrained confidence system allows for varying the impact of specific protein interactions and region annotations on the modeling process. We apply our prediction approach to a large training set using a maximum likelihood method, compare different scoring functions for region interactions and validate the predicted interactions against a collection of experimentally observed interactions. In addition, we analyze prediction performance with respect to the inclusion of different region types, the incorporation of confidence values for training data and the utilization of predicted protein interactions.Contact: mario.albrecht@mpi-inf.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.
Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining
Authors: Yu Shi , Van Vooren Steven , Tranchevent Leon-Charles , De Moor Bart , Moreau Yves 
Published: August 2008

Abstract
Motivation: Computational gene prioritization methods are useful to help identify susceptibility genes potentially being involved in genetic disease. Recently, text mining techniques have been applied to extract prior knowledge from text-based genomic information sources and this knowledge can be used to improve the prioritization process. However, the effect of various vocabularies, representations and ranking algorithms on text mining for gene prioritization is still an issue that requires systematic and comparative studies. Therefore, a benchmark study about the vocabularies, representations and ranking algorithms in gene prioritization by text mining is discussed in this article.
Results: We investigated 5 different domain vocabularies, 2 text representation schemes and 27 linear ranking algorithms for disease gene prioritization by text mining. We indexed 288 177 MEDLINE titles and abstracts with the TXTGate text profiling system and adapted the benchmark dataset of the Endeavour gene prioritization system that consists of 618 disease-causing genes. Textual gene profiles were created and their performance for prioritization were evaluated and discussed in a comparative manner. The results show that inverse document frequency-basedrepresentation of gene term vectors performs better than the term-frequency inverse document-frequency representation. The eVOC and MESH domain vocabularies perform better than Gene Ontology, Online Mendelian Inheritance in Man's and London Dysmorphology Database. The ranking algorithms based on 1-SVM, Standard Correlation and Ward linkage method provide the best performance.
Availability: The MATLAB code of the algorithm and benchmark datasets are available by request.
Contact: shi.yu@esat.kuleuven.be
Supplementary information: Supplementary data are available at Bioinformatics online.
Analysis of segmental duplications via duplication distance
Authors: Kahn Crystal L. , Raphael Benjamin J. 
Published: August 2008

Abstract
Motivation: Segmental duplications are common in mammalian genomes, but their evolutionary origins remain mysterious. A major difficulty in analyzing segmental duplications is that many duplications are complex mosaics of fragments of numerous other segmental duplications.
Results: We introduce a novel measure called duplication distance that describes the minimum number of duplications necessary to create a target string by repeated insertions of fragments of a source string.We derive an efficient algorithm to compute duplication distance, and we use the algorithm to analyze segmental duplications in the human genome. Our analysis reveals possible ancestral relationships between segmental duplications including numerous examples of duplications that contain multiple, nested insertions of fragments from one or more other duplications. Using duplication distance, we also identify a small number of segmental duplications that appear to have seeded many other duplications in the genome, lending support to a two-step model of segmental duplication in the genome.
Availability: Software for computing duplication distance is available upon request.
Contact: clkahn@cs.brown.edu; braphael@cs.brown.edu.
Mining significant tree patterns in carbohydrate sugar chains
Authors: Hashimoto Kosuke , Takigawa Ichigaku , Shiga Motoki , Kanehisa Minoru , Mamitsuka Hiroshi 
Published: August 2008

Abstract
Motivation: Carbohydrate sugar chains or glycans, the third major class of macromolecules, hold branch shaped tree structures. Glycan motifs are known to be two types: (1) conserved patterns called ‘cores’ containing the root and (2) ubiquitous motifs which appear in external parts including leaves and are distributed over different glycan classes. Finding these glycan tree motifs is an important issue, but there have been no computational methods to capture these motifs efficiently.
Results: We have developed an efficient method for mining motifs or significant subtrees from glycans. The key contribution of this method is: (1) to have proposed a new concept, ‘á-closed frequent subtrees’, and an efficient method for mining all these subtrees from given trees and (2) to have proposed to apply statistical hypothesis testing to rerank the frequent subtrees in significance. We experimentally verified the effectiveness of the proposed method using real glycans: (1)We examined the top 10 subtrees obtained by our method at some parameter setting and confirmed that all subtrees are significant motifs in glycobiology. (2) We applied the results of our method to a classification problem and found that our method outperformed other competing methods, SVM with three different tree kernels, being all statistically significant.
Contact: mami@kuicr.kyoto-u.ac.jp
Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry
Authors: Käll Lukas , Storey John D. , Noble William Stafford 
Published: August 2008

AbstractMotivation: A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more difficult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution.Results: We demonstrate how to apply non-parametric logistic regression to this problem. The method makes no explicit assumptions about the form of the underlying score distribution; instead, the method relies upon decoy PSMs, produced by searching the spectra against a decoy sequence database, to provide a model of the null score distribution. We show that our non-parametric logistic regression method produces accurate PEP estimates for six different commonly used PSM score functions. In particular, the estimates produced by our method are comparable in accuracy to those of PeptideProphet, which uses a parametric or semiparametric model designed specifically to work with SEQUEST. The advantage of the non-parametric approach is applicability and robustness to new score functions and new types of data.Availability: C++ code implementing the method as well as supplementary information is available at http://noble.gs.washington.edu/proj/qvalityContact: noble@gs.washington.edu
Functional coherence in domain interaction networks
Authors: Pandey Jayesh , Koyutürk Mehmet , Subramaniam Shankar , Grama Ananth 
Published: August 2008

Abstract
Motivation: Extracting functional information from protein–protein interactions (PPI) poses significant challenges arising from the noisy, incomplete, generic and static nature of data obtained from high-throughput screening. Typical proteins are composed of multiple domains, often regarded as their primary functional and structural units. Motivated by these considerations, domain–domain interactions (DDI) for network-based analyses have received significant recent attention. This article performs a formal comparative investigation of the relationship between functional coherence and topological proximity in PPI and DDI networks. Our investigation provides the necessary basis for continued and focused investigation of DDIs as abstractions for functional characterization and modularization of networks.
Results: We investigate the problem of assessing the functional coherence of two biomolecules (or segments thereof) in a formal framework. We establish essential attributes of admissible measures of functional coherence, and demonstrate that existing, well-accepted measures are ill-suited to comparative analyses involving different entities (i.e. domains versus proteins). We propose a statistically motivated functional similarity measure that takes into account functional specificity as well as the distribution of functional attributes across entity groups to assess functional similarity in a statistically meaningful and biologically interpretable manner. Results on diverse data, including high-throughput and computationally predicted PPIs, as well as structural and computationally inferred DDIs for different organisms show that: (i) the relationship between functional similarity and network proximity is captured in a much more (biologically) intuitive manner by our measure, compared to existing measures and (ii) network proximity and functional similarity are significantly more correlated in DDI networks than in PPI networks, and that structurally determined DDIs provide better functional relevance as compared to computationally inferred DDIs.
Contact: jpandey@cs.purdue.edu