bluuebunny/crossref_metadata_2025_split huggingface dataset:
Binary tree-structured vector quantization approach to
clustering and visualizing microarray data
Authors: Sultan M. , Wigle D.A. , Cumbaa C.A. , Maziarz M. , Glasgow J. , Tsao M.S. , Jurisica I. 
Published: May 2011

Abstract
Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into ‘meaningful’ groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified.
Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach.
Availability: The BTSVQ system is implemented in Matlab R12 using the SOM toolbox for the visualization and preprocessing of the data http://www.cis.hut.fi/projects/somtoolbox/ BTSVQ is available for non-commercial use http://www.uhnres.utoronto.ca/ta3/BTSVQ
Contact: ij@uhnres.utoronto.ca
Keywords: microarray data clustering and visulization; self-organizing maps, partitive k-means clustering; lung cancer.
Linking gene expression data with patient survival times using partial least squares
Authors: Park Peter J. , Tian Lu , Kohane Isaac S. 
Published: May 2011

Abstract
There is an increasing need to link the large amount of genotypic data, gathered using microarrays for example, with various phenotypic data from patients. The classification problem in which gene expression data serve as predictors and a class label phenotype as the binary outcome variable has been examined extensively, but there has been less emphasis in dealing with other types of phenotypic data. In particular, patient survival times with censoring are often not used directly as a response variable due to the complications that arise from censoring.
We show that the issues involving censored data can be circumvented by reformulating the problem as a standard Poisson regression problem. The procedure for solving the transformed problem is a combination of two approaches: partial least squares, a regression technique that is especially effective when there is severe collinearity due to a large number of predictors, and generalized linear regression, which extends standard linear regression to deal with various types of response variables. The linear combinations of the original variables identified by the method are highly correlated with the patient survival times and at the same time account for the variability in the covariates. The algorithm is fast, as it does not involve any matrix decompositions in the iterations. We apply our method to data sets from lung carcinoma and diffuse large B-cell lymphoma studies to verify its effectiveness.
Contact: peter_park@harvard.edu
Keywords: microarrays; generalized linear models; survivial analysis; Poisson regression; principal components analysis.
Statistical process control for large scale microarray experiments
Authors: Model Fabian , König Thomas , Piepenbrock Christian , Adorján Péter 
Published: May 2011

Abstract
Motivation: Maintaining and controlling data quality is a key problem in large scale microarray studies. In particular systematic changes in experimental conditions across multiple chips can seriously affect quality and even lead to false biological conclusions. Traditionally the influence of these effects can be minimized only by expensive repeated measurements, because a detailed understanding of all process relevant parameters seems impossible.
Results: We introduce a novel method for microarray process control that estimates quality based solely on the distribution of the actual measurements without requiring repeated experiments. A robust version of principle component analysis detects single outlier microarrays and thereby enables the use of techniques from multivariate statistical process control. In particular, the T2 control chart reliably tracks undesired changes in process relevant parameters. This can be used to improve the microarray process itself, limits necessary repetitions to only affected samples and therefore maintains quality in a cost effective way. We prove the power of the approach on 3 large sets of DNA methylation microarray data.
Contact: Fabian.Model@epigenomics.com
Keywords: DNA methylation; robust PCA; micorarray; statistical process control; quality control.
Assigning probes into a small number of pools separable by electrophoresis
Authors: Kivioja Teemu , Arvas Mikko , Kataja Kari , Penttilä Merja , Söderlund Hans , Ukkonen Esko 
Published: May 2011

Abstract
Motivation: Measuring transcriptional expression levels (transcriptional profiling) has become one of the most important methods in functional genomics. Still, new measuring methods are needed to obtain more reliable, quantitative data about transcription on a genomic scale. In this paper we concentrate on certain computational optimization problems arising in the design of one such novel method. From a computational point of view the key feature of the new method is that the hybridized probes are distinguished from each other based on their different size. Therefore the probes have to be assigned into pools such that the probes in the same pool have unique sizes different enough from each other. Identification of expressed RNA is given by probe pool and probe size while quantification is given by the label of the probe, e.g. fluorescence intensity.
Results: We show how to computationally find the probes and assign them into pools for a whole genome such that (i) each gene has a specific probe suitable for amplification and hybridization, and (ii) the expression level measurement can be done in a minimal number of pools separable by electrophoresis in order to minimize the total experiment cost of the measurement. Our main result is a polynomial-time approximation algorithm for assigning the probes into pools. We demonstrate the feasibility of the procedure by selecting probes for the yeast genome and assigning them into less than 100 pools. The probe sequences and their assignment into pools are available for academic research on request from the authors.
Contact: Teemu.Kivioja@cs.Helsinki.FI
Keywords: transciptional profiling; hybridization probe selection; graph algorithms; degree-contrained subgraph.
Of truth and pathways: chasing bits of information through myriads of articles
Authors: Krauthammer Michael , Kra Pauline , Iossifov Ivan , Gomez Shawn M. , Hripcsak George , Hatzivassiloglou Vasileios , Friedman Carol , Rzhetsky Andrey 
Published: May 2011

Abstract
Motivation: Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarly hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.
Contact: ar345@columbia.edu
Keywords: statistical modelling; scientometric analysis; molecular interaction data; natural language processing
Statistically based postprocessing of phylogenetic
analysis by clustering
Authors: Stockham Cara , Wang Li-San , Warnow Tandy 
Published: May 2011

Abstract
Motivation: Phylogenetic analyses often produce thousands of candidate trees. Biologists resolve the conflict by computing the consensus of these trees. Single-tree consensus as postprocessing methods can be unsatisfactory due to their inherent limitations.
Results: In this paper we present an alternative approach by using clustering algorithms on the set of candidate trees. We propose bicriterion problems, in particular using the concept of information loss, and new consensus trees called characteristic trees that minimize the information loss. Our empirical study using four biological datasets shows that our approach provides a significant improvement in the information content, while adding only a small amount of complexity. Furthermore, the consensus trees we obtain for each of our large clusters are more resolved than the single-tree consensus trees. We also provide some initial progress on theoretical questions that arise in this context.
Availability: Software available upon request from the authors. The agglomerative clustering is implemented using Matlab (MathWorks, 2000) with the Statistics Toolbox. The Robinson-Foulds distance matrices and the strict consensus trees are computed using PAUP (Swofford, 2001) and the Daniel Huson's tree library on Intel Pentium workstations running Debian Linux.
Contact: lisan@cs.utexas.edu
Supplementary Information: http://www.cs.utexas.edu/users/lisan/ismb02/
Keywords: consensus methods; clustering; phylogenetics; information theory; maximum parsimony.
*To whom correspondence should be addressed.
Efficiently detecting polymorphisms during the fragment
assembly process
Authors: Fasulo Daniel , Halpern Aaron , Dew Ian , Mobarry Clark 
Published: May 2011

Abstract
Motivation: Current genomic sequence assemblers assume that the input data is derived from a single, homogeneous source. However, recent whole-genome shotgun sequencing projects have violated this assumption, resulting in input fragments covering the same region of the genome whose sequences differ due to polymorphic variation in the population. While single-nucleotide polymorphisms (SNPs) do not pose a significant problem to state-of-the-art assembly methods, these methods do not handle insertion/deletion (indel) polymorphisms of more than a few bases.
Results: This paper describes an efficient method for detecting sequence discrepencies due to polymorphism that avoids resorting to global use of more costly, less stringent affine sequence alignments. Instead, the algorithm uses graph-based methods to determine the small set of fragments involved in each polymorphism and performs more sophisticated alignments only among fragments in that set. Results from the incorporation of this method into the Celera Assembler are reported for the D. melanogaster, H. sapiens, and M. musculus genomes.
Availability: The method described herein does not constitute a stand-alone software application, but is laid out in sufficient detail to be implemented as a component of any genomic sequence assembler.
Contact: daniel.fasulo@celera.com
Keywords: whole-genome assembly; shotgun sequencing; polymorphism.
Prediction of contact maps by GIOHMMs and recurrent
neural networks using lateral propagation from all four cardinal
corners
Authors: Pollastri G. , Baldi P. 
Published: May 2011

Abstract
Motivation: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction.
Results: We develop a new set of flexible machine learning architectures for the prediction of contact maps, as well as other information processing and pattern recognition tasks. The architectures can be viewed as recurrent neural network implemantations of a class of Bayesian networks we call generalized input-output HMMs (GIOHMMs). For the specific case of contact maps, contextual information is propagated laterally through four hidden planes, one for each cardinal corner. We show that these architectures can be trained from examples and yield contact map predictors that outperform previously reported methods. While several extensions and improvements are in progress, the current version can accurately predict 60.5% of contacts at a distance cutoff of 8 Å and 45% of distant contacts at 10 Å, for proteins of length up to 300.
Availability: The contact map predictor will be made available through http://promoter.ics.uci.edu/BRNN-PRED/ as part of an existing suite of proteomics predictors.
Contact: gpollast@ics.uci.edupfbaldi@ics.uci.edu
Keywords: protein structure prediction; protein contacts, contact map; graphical models; recurrent neural networks.
*To whom correspondence should be addressed.
Rate4Site: an algorithmic tool for the identification of functional regions
in proteins by surface mapping of evolutionary determinants within their
homologues
Authors: Pupko Tal , Bell Rachel E. , Mayrose Itay , Glaser Fabian , Ben-Tal Nir 
Published: May 2011

Abstract
Motivation: A number of proteins of known three-dimensional (3D) structure exist, with yet unknown function. In light of the recent progress in structure determination methodology, this number is likely to increase rapidly. A novel method is presented here: ‘Rate4Site’, which maps the rate of evolution among homologous proteins onto the molecular surface of one of the homologues whose 3D-structure is known. Functionally important regions often correspond to surface patches of slowly evolving residues.
Results: Rate4Site estimates the rate of evolution of amino acid sites using the maximum likelihood (ML) principle. The ML estimate of the rates considers the topology and branch lengths of the phylogenetic tree, as well as the underlying stochastic process. To demonstrate its potency, we study the Src SH2 domain. Like previously established methods, Rate4Site detected the SH2 peptide-binding groove. Interestingly, it also detected inter-domain interactions between the SH2 domain and the rest of the Src protein that other methods failed to detect.
Availability: Rate4Site can be downloaded at: http://ashtoret.tau.ac.il/ It is implemented as a web server at: bioinfo.tau.ac.il/ConSurf
Contact: tal@ism.ac.jprebell@ashtoret.tau.ac.ilfabian@ashtoret.tau.ac.ilbental@ashtoret.tau.ac.il
Supplementary Information: Multiple sequence alignment of homologous SH2 domains, the corresponding phylogenetic tree and additional examples are available at http://ashtoret.tau.ac.il/~rebell
Keywords: rate variation among sites; evolutionary conservation; protein evolution; maximum likelihood; SH2 domains.
*To whom correspondence should be addressed.
†These authors contributed equally.
A similarity-based method for genome-wide prediction of
disease-relevant human genes
Authors: Freudenberg J. , Propping P. 
Published: May 2011

Abstract
Motivation: A method for prediction of disease relevant human genes from the phenotypic appearance of a query disease is presented. Diseases of known genetic origin are clustered according to their phenotypic similarity. Each cluster entry consists of a disease and its underlying disease gene. Potential disease genes from the human genome are scored by their functional similarity to known disease genes in these clusters, which are phenotypically similar to the query disease.
Results:For assessment of the approach, a leave-one-out cross-validation of 878 diseases from the OMIM database, using 10672 candidate genes from the human genome, is performed. Depending on the applied parameters, in roughly one-third of cases the true solution is contained within the top scoring 3% of predictions and in two-third of cases the true solution is contained within the top scoring 15% of predictions.
The prediction results can either be used to identify target genes, when searching for a mutation in monogenic diseases or for selection of loci in genotyping experiments in genetically complex diseases.
Contact: jan.freudenberg@uni-bonn.de
Breakpoint medians and breakpoint phylogenies: A
fixed-parameter approach
Authors: Gramm Jens , Niedermeier Rolf 
Published: May 2011

Abstract
With breakpoint distance, the genome rearrangement field delivered one of the currently most popular measures in phylogenetic studies for related species. Here, Breakpoint Median, which is NP-complete already for three given species (whose genomes are represented as signed orderings), is the core basic problem. For the important special case of three species, approximation (ratio 7/6) and exact heuristic algorithms were developed. Here, we provide an exact, fixed-parameter algorithm with provable performance bounds. For instance, a breakpoint median for three signed orderings over nelements that causes at most d breakpoints can be computed in time O((2.15)d·n). We show the algorithm's practical usefulness through experimental studies. In particular, we demonstrate that a simple implementation of our algorithm combined with a new tree construction heuristic allows for a new approach to breakpoint phylogeny, yielding evolutionary trees that are competitive in comparison with known results developed in a recent series of papers that use clever algorithm engineering methods.
Stochastic pairwise alignments
Authors: Mückstein U. , Hofacker I.L. , Stadler P.F. 
Published: May 2011

Abstract
Motivation: The level of sequence conservation between related nucleic acids or proteins often varies considerably along the sequence. Both regions with high variability (mutational hot-spots) and regions of almost perfect sequence identity may occur in the same pair of molecules. The reliability of an alignment therefore strongly depends on the level of local sequence similarity. Especially in regions of high variability, many alignments of almost equal quality exist, and the optimal alignment is highly arbitrary.
Results: We discuss two approaches which deal with the inherent ambiguity of the alignment problem based on the computation of the partition function over all canonical pairwise alignments. The ensemble of possible alignments can be described by the probabilities Pij of a match between position i in the first and position j in the second sequence. Alternatively, we introduce a probabilistic backtracking procedure that generates ensembles of suboptimal alignments with correct statistical weights.
A comparison between structure based alignments and large samples of stochastic alignments shows that the ensemble contains correct alignments with significant probabilities even though the optimal alignment deviates significantly from the structural alignment. Ensembles of suboptimal alignments obtained by stochastic backtracking can be used as input to any bioinformatics method based on pairwise alignment in order to gain reliability information not available from a single optimal alignment.
Availability: The software described in this contribution is available for downloading at http://www.tbi.univie.ac.at/~ulim/probA/
Contact: ivo@tbi.univie.ac.at
Selecting targets for therapeutic validation through
differential protein expression using chromatography-mass
spectrometry
Authors: Patterson Scott 
Published: May 2011

Abstract
The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors:
level in steady-state may not be correlated with actual encoded protein levels;
differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and
the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study.
In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.