bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle
Authors: Fauré Adrien , Naldi Aurélien , Chaouiya Claudine , Thieffry Denis 
Published: July 2006

Abstract
Motivation: To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks.
Results: Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge.
Availability: The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (). Higher resolution state transitions graphs are also found on this web site (Model Repository page).
Contact: thieffry@ibdm.univ-mrs.fr
Relative contributions of structural designability and functional diversity in molecular evolution of duplicates
Authors: Shakhnovich Boris E. 
Published: July 2006

Abstract
Analysis of increasingly saturated sequence databases have shown that gene family sizes are highly skewed with many families being small and few containing many, far-diverged homologs. Additionally, recently published results have identified a structural determinant of mutational plasticity: designability that correlates strongly with gene family size. In this paper, we explore the possible links between the two observations, exploring the possible effect of designability on duplication and divergence.
We show that designability has an inverse of expected relationship with strength of selection. More designable domains that should have more mutational plasticity evolve slower. However, we also present evidence that recently duplicated genes have variable probability of locus fixation correlated with strength of selection. As expected, paralogs under stronger evolutionary pressure have a lower failure rate. Finally, we show that probability of pseudogene formation from gene duplication can be directly tied to designability and functional flexibility of the family.
We present evidence that gene families with higher designability have diverged farther because of lower probability of pseudogenization. Additionally, mutational plasticity may play an integral role by influencing pseudogenization rate. Either way, we show that considering the failure rate of duplications is integral in understanding the determinants and dynamics of molecular evolution.
Contact: borya@acs.bu.edu
Semi-supervised LC/MS alignment for differential proteomics
Authors: Fischer Bernd , Grossmann Jonas , Roth Volker , Gruissem Wilhelm , Baginsky Sacha , Buhmann Joachim M. 
Published: July 2006

Abstract
Motivation: Mass spectrometry (MS) combined with high-performance liquid chromatography (LC) has received considerable attention for high-throughput analysis of proteomes. Isotopic labeling techniques such as ICAT [5,6] have been successfully applied to derive differential quantitative information for two protein samples, however at the price of significantly increased complexity of the experimental setup. To overcome these limitations, we consider a label-free setting where correspondences between elements of two samples have to be established prior to the comparative analysis. The alignment between samples is achieved by nonlinear robust ridge regression. The correspondence estimates are guided in a semi-supervised fashion by prior information which is derived from sequenced tandem mass spectra.
Results: The semi-supervised method for finding correspondences was successfully applied to aligning highly complex protein samples, even if they exhibit large variations due to different biological conditions. A large-scale experiment clearly demonstrates that the proposed method bridges the gap between statistical data analysis and label-free quantitative differential proteomics.
Availability: The software will be available on the website
Contact: bernd.fischer@inf.ethz.ch
Improved Pruning algorithms and Divide-and-Conquer strategies for Dead-End Elimination, with application to protein design
Authors: Georgiev Ivelin , Lilien Ryan H. , Donald Bruce R. 
Published: July 2006

Abstract
Motivation: Structure-based protein redesign can help engineer proteins with desired novel function. Improving computational efficiency while still maintaining the accuracy of the design predictions has been a major goal for protein design algorithms. The combinatorial nature of protein design results both from allowing residue mutations and from the incorporation of protein side-chain flexibility. Under the assumption that a single conformation can model protein folding and binding, the goal of many algorithms is the identification of the Global Minimum Energy Conformation (GMEC). A dominant theorem for the identification of the GMEC is Dead-End Elimination (DEE). DEE-based algorithms have proven capable of eliminating the majority of candidate conformations, while guaranteeing that only rotamers not belonging to the GMEC are pruned. However, when the protein design process incorporates rotameric energy minimization, DEE is no longer provably-accurate. Hence, with energy minimization, the minimized-DEE (MinDEE) criterion must be used instead.
Results: In this paper, we present provably-accurate improvements to both the DEE and MinDEE criteria. We show that our novel enhancements result in a speedup of up to a factor of more than 1000 when applied in redesign for three different proteins: Gramicidin Synthetase A, plastocyanin, and protein G.
Availability: Contact authors for source code.
Contact: Bruce.R.Donald@dartmouth.edu
Annotating proteins by mining protein interaction networks
Authors: Kirac Mustafa , Ozsoyoglu Gultekin , Yang Jiong 
Published: July 2006

Abstract
Motivation: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins.
Results: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%.
Availability: Code is available upon request. Results and used materials are available online at
Contact: kirac@case.edu
BaCelLo: a balanced subcellular localization predictor
Authors: Pierleoni Andrea , Martelli Pier Luigi , Fariselli Piero , Casadio Rita 
Published: July 2006

Abstract
Motivation. The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects.
Results. BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment.
Availability. BaCelLo can be accessed at
Contact. casadio@alma.unibo.it, andrea@biocomp.unibo.it, gigi@biocomp.unibo.it, piero@biocomp.unibo.it
A model-based approach for mining membrane protein crystallization trials
Authors: Asur Sitaram , Raman Pichai , Otey Matthew Eric , Parthasarathy Srinivasan 
Published: July 2006

Abstract
Motivation: Membrane proteins are known to play crucial roles in various cellular functions. Information about their function can be derived from their structure, but knowledge of these proteins is limited, as their structures are difficult to obtain. Crystallization has proved to be an essential step in the determination of macromolecular structure. Unfortunately, the bottleneck is that the crystallization process is quite complex and extremely sensitive to experimental conditions, the selection of which is largely a matter of trial and error. Even under the best conditions, it can take a large amount of time, from weeks to years, to obtain diffraction-quality crystals. Other issues include the time and cost involved in taking multiple trials and the presence of very few positive samples in a wide and largely undetermined parameter space. Therefore, any help in directing scientists’ attention to the hot spots in the conceptual crystallization space would lead to increased efficiency in crystallization trials.
Results: This work is an application case study on mining membrane protein crystallization trials to predict novel conditions that have a high likelihood of leading to crystallization. We use suitable supervised learning algorithms to model the data-space and predict a novel set of crystallization conditions. Our preliminary wet laboratory results are very encouraging and we believe this work shows great promise. We conclude with a view of the crystallization space that is based on our results, which should prove useful for future studies in this area.
Contact: Srinivasan Parthasarathy, 693 Dreese Lab, 2015 Neil Ave, Columbus, OH-43210, USA, Email: srini@cse.ohio-state.edu
Peptide sequence tag-based blind identification of post-translational modifications with point process model
Authors: Liu Chunmei , Yan Bo , Song Yinglei , Xu Ying , Cai Liming 
Published: July 2006

Abstract
An important but difficult problem in proteomics is the identification of post-translational modifications (PTMs) in a protein. In general, the process of PTM identification by aligning experimental spectra with theoretical spectra from peptides in a peptide database is very time consuming and may lead to high false positive rate. In this paper, we introduce a new approach that is both efficient and effective for blind PTM identification. Our work consists of the following phases. First, we develop a novel tree decomposition based algorithm that can efficiently generate peptide sequence tags (PSTs) from an extended spectrum graph. Sequence tags are selected from all maximum weighted antisymmetric paths in the graph and their reliabilities are evaluated with a score function. An efficient deterministic finite automaton (DFA) based model is then developed to search a peptide database for candidate peptides by using the generated sequence tags. Finally, a point process model—an efficient blind search approach for PTM identification, is applied to report the correct peptide and PTMs if there are any. Our tests on 2657 experimental tandem mass spectra and 2620 experimental spectra with one artificially added PTM show that, in addition to high efficiency, our ab-initio sequence tag selection algorithm achieves better or comparable accuracy to other approaches. Database search results show that the sequence tags of lengths 3 and 4 filter out more than 98.3% and 99.8% peptides respectively when applied to a yeast peptide database. With the dramatically reduced search space, the point process model achieves significant improvement in accuracy as well.
Availability: The software is available upon request.
Contact: {chunmei,cai}@cs.uga.edu
An integrative approach for causal gene identification and gene regulatory pathway inference
Authors: Tu Zhidong , Wang Li , Arbeitman Michelle N. , Chen Ting , Sun Fengzhu 
Published: July 2006

Abstract
Motivation: Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.
Results: We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)–DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network.
Availability: Source code is available upon request.
Contacts: fsun@usc.edu
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Authors: Borgwardt Karsten M. , Gretton Arthur , Rasch Malte J. , Kriegel Hans-Peter , Schölkopf Bernhard , Smola Alex J. 
Published: July 2006

Abstract
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic.
The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology.
Results: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors.
Conclusions: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments.
Availability:
Contact: kb@dbs.ifi.lmu.de
A probabilistic approach to protein backbone tracing in electron density maps
Authors: DiMaio Frank , Shavlik Jude , Phillips George N. 
Published: July 2006

Abstract
One particularly time-consuming step in protein crystallography is interpreting the electron density map; that is, fitting a complete molecular model of the protein into a 3D image of the protein produced by the crystallographic process. In poor-quality electron density maps, the interpretation may require a significant amount of a crystallographer's time. Our work investigates automating the time-consuming initial backbone trace in poor-quality density maps. We describe ACMI (Automatic Crystallographic Map Interpreter), which uses a probabilistic model known as a Markov field to represent the protein. Residues of the protein are modeled as nodes in a graph, while edges model pairwise structural interactions. Modeling the protein in this manner allows the model to be flexible, considering an almost infinite number of possible conformations, while rejecting any that are physically impossible. Using an efficient algorithm for approximate inference—belief propagation—allows the most probable trace of the protein's backbone through the density map to be determined. We test ACMI on a set of ten protein density maps (at 2.5 to 4.0 Å resolution), and compare our results to alternative approaches. At these resolutions, ACMI offers a more accurate backbone trace than current approaches.
Contact: dimaio@cs.wisc.edu
An equilibrium partitioning model connecting gene expression and cis-motif content
Authors: Mellor Joe , DeLisi Charles 
Published: July 2006

Abstract
Thermodynamic favorability of transcription factor (TF) binding to DNA is a significant factor in the control of gene expression. Theoretical and in vitro measures link the relative equilibrium energy of a particular DNA binding protein to the sequence variation among binding sites in a genome. Extending this principle, we investigate whether biological variation in expression levels of active proteins leads to regulation of different sets of genes, based on inferred affinities of sites upstream of those genes. The TF-concentration-dependent variation in the repertoire of genes regulated by a particular TF is expected to follow patterns of chemical partitioning over DNA sites having differing affinity, and we develop a new modeling approach to test this hypothesis. Based on computational TF binding site discovery and genome-wide expression data available in Saccharomyces cerevisiae, we explore motif content for sets of genes and conditions having varying concentrations of different transcription factors which turn those genes on or off. We find cases of significant correlation between the level of intragenomic motif sequence variation and modeled TF protein levels that actuates regulation of corresponding sets of genes, and discuss the observed TF motif variants for several yeast transcription factors, as well as the potential biological functions of genes that are regulated by differential response to these high and low concentrations of particular TFs. These findings suggest that motif sequences of transcription factor binding sites may often be linked with the expression state of corresponding DNA-binding proteins.
Contact: mellor@bu.edu
Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo
Authors: Qi Yuan , Missiuro Patrycja E. , Kapoor Ashish , Hunter Craig P. , Jaakkola Tommi S. , Gifford David K. , Ge Hui 
Published: July 2006

Abstract
Motivation: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods.
Results: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments.
Availability: The results are available at
Contact: hge@wi.mit.edu or gifford@mit.edu
Create and assess protein networks through molecular characteristics of individual proteins
Authors: Ofran Yanay , Yachdav Guy , Mozes Eyal , Soong Ta-tsen , Nair Rajesh , Rost Burkhard 
Published: July 2006

Abstract
Motivation: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes.
Results: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease.
Availability:
Contact: pinat@rostlab.org; ofran@cubic.bioc.columbia.edu