bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selecting anti-HIV therapies based on a variety of genomic and clinical factors
Authors: Rosen-Zvi Michal , Altmann Andre , Prosperi Mattia , Aharoni Ehud , Neuvirth Hani , Sönnerborg Anders , Schülter Eugen , Struck Daniel , Peres Yardena , Incardona Francesca , Kaiser Rolf , Zazzi Maurizio , Lengauer Thomas 
Published: June 2008

Abstract
Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy.
Results: Three different machine learning techniques were used: generative–discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome.
Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org
Contact: rosen@il.ibm.com
Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model
Authors: Wang Yuan , Zhou Xiaobo , Wang Honghui , Li King , Yao Lixiu , Wong Stephen T.C. 
Published: June 2008

Abstract
Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.
Contact: XZhou@tmhs.org
MACHOS: Markov clusters of homologous subsequences
Authors: Wong Simon , Ragan Mark A. 
Published: June 2008

Abstract
Motivation: The classification of proteins into homologous groups (families) allows their structure and function to be analysed and compared in an evolutionary context. The modular nature of eukaryotic proteins presents a considerable challenge to the delineation of families, as different local regions within a single protein may share common ancestry with distinct, even mutually exclusive, sets of homologs, thereby creating an intricate web of homologous relationships if full-length sequences are taken as the unit of evolution. We attempt to disentangle this web by developing a fully automated pipeline to delineate protein subsequences that represent sensible units for homology inference, and clustering them into putatively homologous families using the Markov clustering algorithm.
Results: Using six eukaryotic proteomes as input, we clustered 162 349 protein sequences into 19 697–77 415 subsequence families depending on granularity of clustering. We validated these Markov clusters of homologous subsequences (MACHOS) against the manually curated Pfam domain families, using a quality measure to assess overlap. Our subsequence families correspond well to known domain families and achieve higher quality scores than do groups generated by fully automated domain family classification methods. We illustrate our approach by analysis of a group of proteins that contains the glutamyl/glutaminyl-tRNA synthetase domain, and conclude that our method can produce high-coverage decomposition of protein sequence space into precise homologous families in a way that takes the modularity of eukaryotic proteins into account. This approach allows for a fine-scale examination of evolutionary histories of proteins encoded in eukaryotic genomes.
Contact: m.ragan@imb.uq.edu.au
Supplementary information: Supplementary data are available at Bioinformatics online. MACHOS for the six proteomes are available as FASTA-formatted files: http://research1t.imb.uq.edu.au/ragan/machos
Guided genome halving: hardness, heuristics and the history of the Hemiascomycetes
Authors: Zheng Chunfang , Zhu Qian , Adam Zaky , Sankoff David 
Published: June 2008

Abstract
Motivation: Some present day species have incurred a whole genome doubling event in their evolutionary history, and this is reflected today in patterns of duplicated segments scattered throughout their chromosomes. These duplications may be used as data to ‘halve’ the genome, i.e. to reconstruct the ancestral genome at the moment of doubling, but the solution is often highly nonunique. To resolve this problem, we take account of outgroups, external reference genomes, to guide and narrow down the search.
Results: We improve on a previous, computationally costly, ‘brute force’ method by adapting the genome halving algorithm of El-Mabrouk and Sankoff so that it rapidly and accurately constructs an ancestor close the outgroups, prior to a local optimization heuristic. We apply this to reconstruct the predoubling ancestor of Saccharomyces cerevisiae and Candida glabrata, guided by the genomes of three other yeasts that diverged before the genome doubling event. We analyze the results in terms (1) of the minimum evolution criterion, (2) how close the genome halving result is to the final (local) minimum and (3) how close the final result is to an ancestor manually constructed by an expert with access to additional information. We also visualize the set of reconstructed ancestors using classic multidimensional scaling to see what aspects of the two doubled and three unduplicated genomes influence the differences among the reconstructions.
Availability: The experimental software is available on request.
Contact: sankoff@uottawa.ca
Estimating true evolutionary distances under the DCJ model
Authors: Lin Yu , Moret Bernard M.E. 
Published: June 2008

AbstractMotivation: Modern techniques can yield the ordering and strandedness of genes on each chromosome of a genome; such data already exists for hundreds of organisms. The evolutionary mechanisms through which the set of the genes of an organism is altered and reordered are of great interest to systematists, evolutionary biologists, comparative genomicists and biomedical researchers. Perhaps the most basic concept in this area is that of evolutionary distance between two genomes: under a given model of genomic evolution, how many events most likely took place to account for the difference between the two genomes?Results: We present a method to estimate the true evolutionary distance between two genomes under the ‘double-cut-and-join’ (DCJ) model of genome rearrangement, a model under which a single multichromosomal operation accounts for all genomic rearrangement events: inversion, transposition, translocation, block interchange and chromosomal fusion and fission. Our method relies on a simple structural characterization of a genome pair and is both analytically and computationally tractable. We provide analytical results to describe the asymptotic behavior of genomes under the DCJ model, as well as experimental results on a wide variety of genome structures to exemplify the very high accuracy (and low variance) of our estimator. Our results provide a tool for accurate phylogenetic reconstruction from multichromosomal gene rearrangement data as well as a theoretical basis for refinements of the DCJ model to account for biological constraints.Availability: All of our software is available in source form under GPL at http://lcbb.epfl.chContact: bernard.moret@epfl.ch
The multiple gene duplication problem revisited
Authors: Bansal Mukul S. , Eulenstein Oliver 
Published: June 2008

Abstract
Motivation: Deciphering the location of gene duplications and multiple gene duplication episodes on the Tree of Life is fundamental to understanding the way gene families and genomes evolve. The multiple gene duplication problem provides a framework for placing gene duplication events onto nodes of a given species tree, and detecting episodes of multiple gene duplication. One version of the multiple gene duplication problem was defined by Guigó et al. in 1996. Several heuristic solutions have since been proposed for this problem, but no exact algorithms were known.
Results: In this article we solve this longstanding open problem by providing the first exact and efficient solution. We also demonstrate the improvement offered by our algorithm over the best heuristic approaches, by applying it to several simulated as well as empirical datasets.
Contact: oeulenst@cs.iastate.edu
Nucleosome positioning from tiling microarray data
Authors: Yassour Moran , Kaplan Tommy , Jaimovich Ariel , Friedman Nir 
Published: June 2008

AbstractMotivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5–10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed.Results: In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions.We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA.Contact: nir@cs.huji.ac.il
Alignment and classification of time series gene expression in clinical studies
Authors: Lin Tien-ho , Kaminski Naftali , Bar-Joseph Ziv 
Published: June 2008

Abstract
Motivation: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class).
Results: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers.
Availability: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/
Contact: zivbj@cs.cmu.edu
Towards the use of argumentation in bioinformatics: a gene expression case study
Authors: McLeod Kenneth , Burger Albert 
Published: June 2008

Abstract
Motivation: Due to different experimental setups and various interpretations of results, the data contained in online bioinformatics resources can be inconsistent, therefore, making it more difficult for users of these resources to assess the suitability and correctness of the answers to their queries. This work investigates the role of argumentation systems to help users evaluate such answers. More specifically, it looks closely at a gene expression case study, creating an appropriate representation of the underlying data and series of rules that are used by a third-party argumentation engine to reason over the query results provided by the mouse gene expression database EMAGE.
Results: A prototype using the ASPIC argumentation engine has been implemented and a preliminary evaluation carried out. This evaluation suggested that argumentation can be used to deal with inconsistent data in biological resources.
Availability: The ASPIC argumentation engine is available from http://www.argumentation.org. EMAGE gene expression data can be obtained from http://genex.hgu.mrc.ac.uk. The argumentation rules for the gene expression example are available from the lead author upon request.
Contact: kcm1@hw.ac.uk
Assessing the functional structure of genomic data
Authors: Huttenhower C. , Troyanskaya O.G. 
Published: June 2008

Abstract
Motivation: The availability of genome-scale data has enabled an abundance of novel analysis techniques for investigating a variety of systems-level biological relationships. As thousands of such datasets become available, they provide an opportunity to study high-level associations between cellular pathways and processes. This also allows the exploration of shared functional enrichments between diverse biological datasets, and it serves to direct experimenters to areas of low data coverage or with high probability of new discoveries.
Results: We analyze the functional structure of Saccharomyces cerevisiae datasets from over 950 publications in the context of over 140 biological processes. This includes a coverage analysis of biological processes given current high-throughput data, a data-driven map of associations between processes, and a measure of similar functional activity between genome-scale datasets. This uncovers subtle gene expression similarities in three otherwise disparate microarray datasets due to a shared strain background. We also provide several means of predicting areas of yeast biology likely to benefit from additional high-throughput experimental screens.
Availability: Predictions are provided in supplementary tables; software and additional data are available from the authors by request.
Contact: ogt@princeton.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
Authors: Yamanishi Yoshihiro , Araki Michihiro , Gutteridge Alex , Honda Wataru , Kanehisa Minoru 
Published: June 2008

Abstract
Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently.
Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery.
Availability: Softwares are available upon request.
Contact: Yoshihiro.Yamanishi@ensmp.fr
Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
Protein complex identification by supervised graph local clustering
Authors: Qi Yanjun , Balem Fernanda , Faloutsos Christos , Klein-Seetharaman Judith , Bar-Joseph Ziv 
Published: June 2008

Abstract
Motivation: Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphs representing protein complexes. Previous methods for performing such search relied on the assumption that complexes form a clique in that graph. While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure.
Results: We present an algorithm for inferring protein complexes from weighted interaction graphs. By using graph topological patterns and biological properties as features, we model each complex subgraph by a probabilistic Bayesian network (BN). We use a training set of known complexes to learn the parameters of this BN model. The log-likelihood ratio derived from the BN is then used to score subgraphs in the protein interaction graph and identify new complexes. We applied our method to protein interaction data in yeast. As we show our algorithm achieved a considerable improvement over clique based algorithms in terms of its ability to recover known complexes. We discuss some of the new complexes predicted by our algorithm and determine that they likely represent true complexes.
Availability: Matlab implementation is available on the supporting website: www.cs.cmu.edu/~qyj/SuperComplex
Contact: zivbj@cs.cmu.edu
The effectiveness of position- and composition-specific gap costs for protein similarity searches
Authors: Stojmirović Aleksandar , Gertz E. Michael , Altschul Stephen F. , Yu Yi-Kuo 
Published: June 2008

Abstract
Motivation: The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.
Results: We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program.
Availability: The scripts for performing evaluations are available upon request from the authors.
Contact: yyu@ncbi.nlm.nih.gov
Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space
Authors: Loewenstein Yaniv , Portugaly Elon , Fromer Menachem , Linial Michal 
Published: June 2008

Abstract
Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets.
Application: We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation.
Results: We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families.
Availability: A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request.
Contact: lonshy@cs.huji.ac.il