bluuebunny/crossref_metadata_2025_split huggingface dataset:
CONTRAfold: RNA secondary structure prediction without physics-based models
Authors: Do Chuong B. , Woods Daniel A. , Batzoglou Serafim 
Published: July 2006

Abstract
Motivation: For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondary structure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models.
Results: In this paper, we present CONTRAfold, a novel secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. In a series of cross-validation experiments, we show that grammar-based secondary structure prediction methods formulated as CLLMs consistently outperform their SCFG analogs. Furthermore, CONTRAfold, a CLLM incorporating most of the features found in typical thermodynamic models, achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. Our result thus closes the gap between probabilistic and thermodynamic models, demonstrating that statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters for RNA secondary structure prediction.
Availability: Source code for CONTRAfold is available at .
Contact: chuongdo@cs.stanford.edu
ARTS: accurate recognition of transcription starts in human
Authors: Sonnenburg Sören , Zien Alexander , Rätsch Gunnar 
Published: July 2006

Abstract
We develop new methods for finding transcription start sites (TSS) of RNA Polymerase II binding genes in genomic DNA sequences. Employing Support Vector Machines with advanced sequence kernels, we achieve drastically higher prediction accuracies than state-of-the-art methods.
Motivation: One of the most important features of genomic DNA are the protein-coding genes. While it is of great value to identify those genes and the encoded proteins, it is also crucial to understand how their transcription is regulated. To this end one has to identify the corresponding promoters and the contained transcription factor binding sites. TSS finders can be used to locate potential promoters. They may also be used in combination with other signal and content detectors to resolve entire gene structures.
Results: We have developed a novel kernel based method – called ARTS – that accurately recognizes transcription start sites in human. The application of otherwise too computationally expensive Support Vector Machines was made possible due to the use of efficient training and evaluation techniques using suffix tries. In a carefully designed experimental study, we compare our TSS finder to state-of-the-art methods from the literature: McPromoter, Eponine and FirstEF. For given false positive rates within a reasonable range, we consistently achieve considerably higher true positive rates. For instance, ARTS finds about 35% true positives at a false positive rate of 1/1000, where the other methods find about a half (18%).
Availability: Datasets, model selection results, whole genome predictions, and additional experimental results are available at
Contact: Gunnar.Raetsch@tuebingen.mpg.de
Learning MHC I—peptide binding
Authors: Jojic Nebojsa , Reyes-Gomez Manuel , Heckerman David , Kadie Carl , Schueler-Furman Ora 
Published: July 2006

Abstract
Motivation and results: Motivated by the ability of a simple threading approach to predict MHC I—peptide binding, we developed a new and improved structure-based model for which parameters can be estimated from additional sources of data about MHC-peptide binding. In addition to the known 3D structures of a small number of MHC-peptide complexes that were used in the original threading approach, we included three other sources of information on peptide-MHC binding: (1) MHC class I sequences; (2) known binding energies for a large number of MHC-peptide complexes; and (3) an even larger binary dataset that contains information about strong binders (epitopes) and non-binders (peptides that have a low affinity for a particular MHC molecule). Our model significantly outperforms the standard threading approach in binding energy prediction. In our approach, which we call adaptive double threading, the parameters of the threading model are learnable, and both MHC and peptide sequences can be threaded onto structures of other alleles. These two properties make our model appropriate for predicting binding for alleles for which very little data (if any) is available beyond just their sequence, including prediction for alleles for which 3D structures are not available. The ability of our model to generalize beyond the MHC types for which training data is available also separates our approach from epitope prediction methods which treat MHC alleles as symbolic types, rather than biological sequences. We used the trained binding energy predictor to study viral infections in 246 HIV patients from the West Australian cohort, and over 1000 sequences in HIV clade B from Los Alamos National Laboratory database, capturing the course of HIV evolution over the last 20 years. Finally, we illustrate short-, medium-, and long-term adaptation of HIV to the human immune system.
Availability:
Contact: jojic@microsoft.com
Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data
Authors: Hertel Jana , Stadler Peter F. 
Published: July 2006

Abstract
Summary: Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes.
Availability: The program RNAmicro is available as source code and can be downloaded from
Contact:Jana Hertel, Tel: ++49 341 97 16704, Fax: ++49 341 97 16709, {jana,studla}@bioinf.uni-leipzig.de
Distance based algorithms for small biomolecule classification and structural similarity search
Authors: Karakoc Emre , Cherkasov Artem , Sahinalp S. Cenk 
Published: July 2006

Abstract
Motivation: Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds wrt the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds.
Results: In this paper we focus on the k-nearest-neighbor search method, which, until recently was not considered for small molecule classification. The few recent applications of k-nn to compound classification focus on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance Lp. Here we show how to computationally design the optimal weighted Minkowski distance wLp for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. We then show how to construct pruning based k-nn search data structures for any wLp distance that minimizes similarity search time.
The accuracy achieved by our classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, our classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, our classifier quantifies the level of bioactivity rather than returning a binary decision and thus is more informative than the ANN approach.
Contact: cenk@cs.sfu.ca
Dense subgraph computation via stochastic search: application to detect transcriptional modules
Authors: Everett Logan , Wang Li-San , Hannenhalli Sridhar 
Published: July 2006

Abstract
Motivation: In a tri-partite biological network of transcription factors, their putative target genes, and the tissues in which the target genes are differentially expressed, a tightly inter-connected (dense) subgraph may reveal knowledge about tissue specific transcription regulation mediated by a specific set of transcription factors—a tissue-specific transcriptional module. This is just one context in which an efficient computation of dense subgraphs in a multi-partite graph is needed.
Result: Here we report a generic stochastic search based method to compute dense subgraphs in a graph with an arbitrary number of partitions and an arbitrary connectivity among the partitions. We then use the tool to explore tissue-specific transcriptional regulation in the human genome. We validate our findings in Skeletal muscle based on literature. We could accurately deduce biological processes for transcription factors via the tri-partite clusters of transcription factors, genes, and the functional annotation of genes. Additionally, we propose a few previously unknown TF-pathway associations and tissue-specific roles for certain pathways. Finally, our combined analysis of Cardiac, Skeletal, and Smooth muscle data recapitulates the evolutionary relationship among the three tissues.
Contact: sridharh@pcbi.upenn.edu
Accessing bioscience images from abstract sentences
Authors: Yu Hong , Lee Minsuk 
Published: July 2006

Abstract
Images (e.g., figures) are important experimental results that are typically reported in bioscience full-text articles. Biologists need to access images to validate research facts and to formulate or to test novel research hypotheses. On the other hand, biologists live in an age of information explosion. As thousands of biomedical articles are published every day, systems that help biologists efficiently access images in literature would greatly facilitate biomedical research. We hypothesize that much of image content reported in a full-text article can be summarized by the sentences in the abstract of the article. In our study, more than one hundred biologists had tested this hypothesis and more than 40 biologists had evaluated a novel user-interface BioEx that allows biologists to access images directly from abstract sentences. Our results show that 87.8% biologists were in favor of BioEx over two other baseline user-interfaces. We further developed systems that explored hierarchical clustering algorithms to automatically identify abstract sentences that summarize the images. One of the systems achieves a precision of 100% that corresponds to a recall of 4.6%.
Contact: hongyu@uwm.edu or hy52@columbia.edu
Identification of function-associated loop motifs and application to protein function prediction
Authors: Espadaler Jordi , Querol Enrique , Aviles Francesc X. , Oliva Baldo 
Published: July 2006

Abstract
Motivation: The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations.
Results: We evaluated three different scoring methods for their ability to identify known motifs from the PROSITE database in ArchDB. More than 500 new putative function-related motifs not reported in PROSITE were identified. Sequence patterns derived from these motifs were especially useful at predicting precise annotations. The number of reliable sequence annotations could be increased up to 100% with respect to standard BLAST.
Contact: boliva@imim.es
Supplementary information: Supplementary Data are available at Bioinformatics online.
Semi-supervised learning via penalized mixture model with application to microarray sample classification
Authors: Pan Wei , Shen Xiaotong , Jiang Aixiang , Hebbel Robert P. 
Published: July 2006

AbstractMotivation: It is biologically interesting to address whether human blood outgrowth endothelial cells (BOECs) belong to or are closer to large vessel endothelial cells (LVECs) or microvascular endothelial cells (MVECs) based on global expression profiling. An earlier analysis using a hierarchical clustering and a small set of genes suggested that BOECs seemed to be closer to MVECs. By taking advantage of the two known classes, LVEC and MVEC, while allowing BOEC samples to belong to either of the two classes or to form their own new class, we take a semi-supervised learning approach; for high-dimensional data as encountered here, we propose a penalized mixture model with a weighted L1 penalty to realize automatic feature selection while fitting the model.Results: We applied our penalized mixture model to a combined dataset containing 27 BOEC, 28 LVEC and 25 MVEC samples. Analysis results indicated that the BOEC samples appeared to form their own new class. A simulation study confirmed that, compared with the standard mixture model with or without initial variable selection, the penalized mixture model performed much better in identifying relevant genes and forming corresponding clusters. The penalized mixture model seems to be promising for high-dimensional data with the capability of novel class discovery and automatic feature selection.Contact: weip@biostat.umn.edu
Prediction of mRNA polyadenylation sites by support vector machine
Authors: Cheng Yiming , Miura Robert M. , Tian Bin 
Published: July 2006

Abstract
mRNA polyadenylation is responsible for the 3′ end formation of most mRNAs in eukaryotic cells and is linked to termination of transcription. Prediction of mRNA polyadenylation sites [poly(A) sites] can help identify genes, define gene boundaries, and elucidate regulatory mechanisms. Current methods for poly(A) site prediction achieve moderate sensitivity and specificity. Here, we present a method using support vector machine for poly(A) site prediction. Using 15 cis-regulatory elements that are over-represented in various regions surrounding poly(A) sites, this method achieves higher sensitivity and similar specificity when compared with polyadq, a common tool for poly(A) site prediction. In addition, we found that while the polyadenylation signal AAUAAA and U-rich elements are primary determinants for poly(A) site prediction, other elements contribute to both sensitivity and specificity of the prediction, indicating a combinatorial mechanism involving multiple elements when choosing poly(A) sites in human cells.
Contact: btian@umdnj.edu
A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity
Authors: Ferraro E. , Via A. , Ausiello G. , Helmer-Citterich M. 
Published: July 2006

AbstractMotivation: Unravelling the rules underlying protein–protein and protein–ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein–protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain–peptide interactions.Results: We focused on the SH3 domain family and developed a machine-learning approach for inferring interaction specificity. SH3 domains are well-studied PRMs which typically bind proline-rich short sequences characterized by the PxxP consensus. The binding information is known to be held in the conformation of the domain surface and in the short sequence of the peptide. Our method relies on interaction data from high-throughput techniques and benefits from the integration of sequence and structure data of the interacting partners. Here, we propose a novel encoding technique aimed at representing binding information on the basis of the domain–peptide contact residues in complexes of known structure. Remarkably, the new encoding requires few variables to represent an interaction, thus avoiding the ‘curse of dimension’. Our results display an accuracy &gt;90% in detecting new binders of known SH3 domains, thus outperforming neural models on standard binary encodings, profile methods and recent statistical predictors. The method, moreover, shows a generalization capability, inferring specificity of unknown SH3 domains displaying some degree of similarity with the known data.Contacts: enrico@cbm.bio.uniroma2.itSupplementary information: Supplementary data are available at Bioinformatics online.
Bio-Ontology and text: bridging the modeling gap
Authors: Friedman Carol , Borlawsky Tara , Shagina Lyudmila , Xing H. Rosie , Lussier Yves A. 
Published: July 2006

AbstractMotivation: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text.Results: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86–93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes).Availability:Contact: Friedman@dbmi.columbia.edu or Lussier@uchicago.edu
AliBaba: PubMed as a graph
Authors: Plake Conrad , Schiemann Torsten , Pankalla Marcus , Hakenberg Jörg , Leser Ulf 
Published: July 2006

Abstract
The biomedical literature contains a wealth of information on associations between many different types of objects, such as protein–protein interactions, gene–disease associations and subcellular locations of proteins. When searching such information using conventional search engines, e.g. PubMed, users see the data only one-abstract at a time and ‘hidden’ in natural language text. AliBaba is an interactive tool for graphical summarization of search results. It parses the set of abstracts that fit a PubMed query and presents extracted information on biomedical objects and their relationships as a graphical network. AliBaba extracts associations between cells, diseases, drugs, proteins, species and tissues. Several filter options allow for a more focused search. Thus, researchers can grasp complex networks described in various articles at a glance.
Availability:
Contact: hakenberg@informatik.hu-berlin.de
Dipping their big toe into the blogosphere
Authors: Jackson Nigel 
Published: July 2006

PurposeThe purpose of this paper is to examine the use of weblogs by political parties in the 2005 general election campaign. It seeks to identify why, why not, and how parties used their weblogs during the election campaign.Design/methodology/approachThe weblogs of the five political parties which had a weblog were assessed, and eight party e‐campaigners were interviewed.FindingsThe findings contrast with those of studies of the 2004 US presidential campaign where blogs appeared to play a significant campaigning role. Rather, in the UK, party blogs were essentially used as one‐way communication channels which added colour to party web sites. As a result, such weblogs may have encouraged visitors to return because of some form of voyeurism, but they were not either effective conversational, campaigning, or promotional tools.Research limitations/implicationsThe paper is based on a UK general election of an experimental political communication channel.Practical implicationsSuggests the key elements required for the effective use of weblogs. Also suggests that individual candidate weblogs may be a more appropriate channel to reach electors than party‐controlled weblogs. The motivation for using a weblog seems to be essentially a judgement that it might be worthwhile experimenting with one. However, until there is significant evidence that weblogs can have a tangible effect, it is likely that they will remain merely part of the background to a UK general election campaign.Originality/valueThe paper provides a means of judging the value of weblogs within political communication by political actors.
Seen and heard: duality at the access points to television databases
Authors: Caldera‐Serrano Jorge , Zapico‐Alonso Felipe 
Published: July 2006

PurposeTo show how the double nature of audiovisual material, that it is viewed and referenced, is reflected at the access points of a documentary database.Design/methodology/approachTo achieve this aim a journey through the indexation elements – supported by explanatory examples – is undertaken, so as to identify and analyze onomastic, thematic, geographical and chronological aspects.FindingsThe findings reveal the need to identify in indexation the elements viewed in the image track and referenced in the sound track; such a need is supported by user/journalist requests, so that the documentation service is adapted to such information needs.Research limitationsThe management method displayed is only valid for medium and large television companies. This derives from the complexity and cost of human resources and time dedicated.Practical implicationsThe practical value lies in the fact that the best proven structure for indexation of TV audiovisual documents is shown, with this being a useful and agile guide for the implantation of information services.Originality/valueThere are few studies that develop the documentation management of audiovisual documents, of those that count on valid work that stems from the study of indexation in television.