bluuebunny/crossref_metadata_2025_split huggingface dataset:
Personalized identification of altered pathways in cancer using accumulated normal tissue data
Authors: Ahn TaeJin , Lee Eunjin , Huh Nam , Park Taesung 
Published: August 2014

Abstract
Motivation: Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individual’s pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual are often unavailable in clinical situation. Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient’s matched normal data are unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it with accumulated normal samples. We propose and examine personalized extensions of pathway statistics, overrepresentation analysis and functional class scoring, to generate individualized pathway aberrance score.
Results: Collected microarray data of normal tissue of lung and colon mucosa are served as reference to investigate a number of cancer individuals of lung adenocarcinoma (LUAD) and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival-related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified ‘amino acid synthesis and interconversion’ pathway is a good indicator of LUAD (Area Under the Curve (AUC) 0.982 at independent validation). Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.
Availability and implementation: The method was implemented using the R software, available at our Web site: http://bibs.snu.ac.kr/ipas.
Contact: tspark@stat.snu.ac.kr or namhuh@samsung.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Effects of small particle numbers on long-term behaviour in discrete biochemical systems
Authors: Kreyssig Peter , Wozar Christian , Peter Stephan , Veloz Tomás , Ibrahim Bashar , Dittrich Peter 
Published: August 2014

Abstract
Motivation: The functioning of many biological processes depends on the appearance of only a small number of a single molecular species. Additionally, the observation of molecular crowding leads to the insight that even a high number of copies of species do not guarantee their interaction. How single particles contribute to stabilizing biological systems is not well understood yet. Hence, we aim at determining the influence of single molecules on the long-term behaviour of biological systems, i.e. whether they can reach a steady state.
Results: We provide theoretical considerations and a tool to analyse Systems Biology Markup Language models for the possibility to stabilize because of the described effects. The theory is an extension of chemical organization theory, which we called discrete chemical organization theory. Furthermore we scanned the BioModels Database for the occurrence of discrete chemical organizations. To exemplify our method, we describe an application to the Template model of the mitotic spindle assembly checkpoint mechanism.
Availability and implementation: http://www.biosys.uni-jena.de/Services.html.
Contact: bashar.ibrahim@uni-jena.de or dittrich@minet.uni-jena.de
Supplementary information: Supplementary data are available at Bioinformatics online.
A new statistical framework to assess structural alignment quality using information compression
Authors: Collier James H. , Allison Lloyd , Lesk Arthur M. , Garcia de la Banda Maria , Konagurthu Arun S. 
Published: August 2014

Abstract
Motivation: Progress in protein biology depends on the reliability of results from a handful of computational techniques, structural alignments being one. Recent reviews have highlighted substantial inconsistencies and differences between alignment results generated by the ever-growing stock of structural alignment programs. The lack of consensus on how the quality of structural alignments must be assessed has been identified as the main cause for the observed differences. Current methods assess structural alignment quality by constructing a scoring function that attempts to balance conflicting criteria, mainly alignment coverage and fidelity of structures under superposition. This traditional approach to measuring alignment quality, the subject of considerable literature, has failed to solve the problem. Further development along the same lines is unlikely to rectify the current deficiencies in the field.
Results: This paper proposes a new statistical framework to assess structural alignment quality and significance based on lossless information compression. This is a radical departure from the traditional approach of formulating scoring functions. It links the structural alignment problem to the general class of statistical inductive inference problems, solved using the information-theoretic criterion of minimum message length. Based on this, we developed an efficient and reliable measure of structural alignment quality, I-value. The performance of I-value is demonstrated in comparison with a number of popular scoring functions, on a large collection of competing alignments. Our analysis shows that I-value provides a rigorous and reliable quantification of structural alignment quality, addressing a major gap in the field.
Availability: http://lcb.infotech.monash.edu.au/I-value
Contact: arun.konagurthu@monash.edu
Supplementary information: Online supplementary data are available at http://lcb.infotech.monash.edu.au/I-value/suppl.html
Point estimates in phylogenetic reconstructions
Authors: Benner Philipp , Bačák Miroslav , Bourguignon Pierre-Yves 
Published: August 2014

AbstractMotivation: The construction of statistics for summarizing posterior samples returned by a Bayesian phylogenetic study has so far been hindered by the poor geometric insights available into the space of phylogenetic trees, and ad hoc methods such as the derivation of a consensus tree makeup for the ill-definition of the usual concepts of posterior mean, while bootstrap methods mitigate the absence of a sound concept of variance. Yielding satisfactory results with sufficiently concentrated posterior distributions, such methods fall short of providing a faithful summary of posterior distributions if the data do not offer compelling evidence for a single topology.Results: Building upon previous work of Billera et al., summary statistics such as sample mean, median and variance are defined as the geometric median, Fréchet mean and variance, respectively. Their computation is enabled by recently published works, and embeds an algorithm for computing shortest paths in the space of trees. Studying the phylogeny of a set of plants, where several tree topologies occur in the posterior sample, the posterior mean balances correctly the contributions from the different topologies, where a consensus tree would be biased. Comparisons of the posterior mean, median and consensus trees with the ground truth using simulated data also reveals the benefits of a sound averaging method when reconstructing phylogenetic trees.Availability and implementation: We provide two independent implementations of the algorithm for computing Fréchet means, geometric medians and variances in the space of phylogenetic trees. TFBayes: https://github.com/pbenner/tfbayes, TrAP: https://github.com/bacak/TrAP.Contact: philipp.benner@mis.mpg.de
Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm
Authors: Yan Jingwen , Du Lei , Kim Sungeun , Risacher Shannon L. , Huang Heng , Moore Jason H. , Saykin Andrew J. , Shen Li ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: August 2014

AbstractMotivation: Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP–multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge.Results: The proposed KG-SCCA method is able to model two types of prior knowledge: one as a group structure (e.g. linkage disequilibrium blocks among SNPs) and the other as a network structure (e.g. gene co-expression network among brain regions). The new model incorporates these prior structures by introducing new regularization terms to encourage weight similarity between grouped or connected features. A new algorithm is designed to solve the KG-SCCA model without imposing the independence constraint on the input features. We demonstrate the effectiveness of our algorithm with both synthetic and real data. For real data, using an Alzheimer’s disease (AD) cohort, we examine the imaging genetic associations between all SNPs in the APOE gene (i.e. top AD gene) and amyloid deposition measures among cortical regions (i.e. a major AD hallmark). In comparison with a widely used SCCA implementation, our KG-SCCA algorithm produces not only improved cross-validation performances but also biologically meaningful results.Availability: Software is freely available on request.Contact: shenli@iu.edu
ContrastRank: a new method for ranking putative cancer driver genes and classification of tumor samples
Authors: Tian Rui , Basu Malay K. , Capriotti Emidio 
Published: August 2014

Abstract
Motivation: The recent advance in high-throughput sequencing technologies is generating a huge amount of data that are becoming an important resource for deciphering the genotype underlying a given phenotype. Genome sequencing has been extensively applied to the study of the cancer genomes. Although a few methods have been already proposed for the detection of cancer-related genes, their automatic identification is still a challenging task. Using the genomic data made available by The Cancer Genome Atlas Consortium (TCGA), we propose a new prioritization approach based on the analysis of the distribution of putative deleterious variants in a large cohort of cancer samples.
Results: In this paper, we present ContastRank, a new method for the prioritization of putative impaired genes in cancer. The method is based on the comparison of the putative defective rate of each gene in tumor versus normal and 1000 genome samples. We show that the method is able to provide a ranked list of putative impaired genes for colon, lung and prostate adenocarcinomas. The list significantly overlaps with the list of known cancer driver genes previously published. More importantly, by using our scoring approach, we can successfully discriminate between TCGA normal and tumor samples. A binary classifier based on ContrastRank score reaches an overall accuracy &gt;90% and the area under the curve (AUC) of receiver operating characteristics (ROC) &gt;0.95 for all the three types of adenocarcinoma analyzed in this paper. In addition, using ContrastRank score, we are able to discriminate the three tumor types with a minimum overall accuracy of 77% and AUC of 0.83.
Conclusions: We describe ContrastRank, a method for prioritizing putative impaired genes in cancer. The method is based on the comparison of exome sequencing data from different cohorts and can detect putative cancer driver genes.
ContrastRank can also be used to estimate a global score for an individual genome about the risk of adenocarcinoma based on the genetic variants information from a whole-exome VCF (Variant Calling Format) file. We believe that the application of ContrastRank can be an important step in genomic medicine to enable genome-based diagnosis.
Availability and implementation: The lists of ContrastRank scores of all genes in each tumor type are available as supplementary materials. A webserver for evaluating the risk of the three studied adenocarcinomas starting from whole-exome VCF file is under development.
Contact: emidio@uab.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action
Authors: Schroeder Michael P. , Rubio-Perez Carlota , Tamborero David , Gonzalez-Perez Abel , Lopez-Bigas Nuria 
Published: August 2014

Abstract
Motivation: Several computational methods have been developed to identify cancer drivers genes—genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role.
Result: OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role.
Availability and implementation: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role.
Contact: abel.gonzalez@upf.edu or nuria.lopez@upf.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Integration of molecular network data reconstructs Gene Ontology
Authors: Gligorijević Vladimir , Janjić Vuk , Pržulj Nataša 
Published: August 2014

Abstract
Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data.
Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling.
Availability and implementation: Supplementary Tables of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic.ac.uk/GO-Reconstruction/.
Contact: natasha@imperial.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
Extracting patterns of database and software usage from the bioinformatics literature
Authors: Duck Geraint , Nenadic Goran , Brass Andy , Robertson David L. , Stevens Robert 
Published: August 2014

Abstract
Motivation: As a natural consequence of being a computer-based discipline, bioinformatics has a strong focus on database and software development, but the volume and variety of resources are growing at unprecedented rates. An audit of database and software usage patterns could help provide an overview of developments in bioinformatics and community common practice, and comparing the links between resources through time could demonstrate both the persistence of existing software and the emergence of new tools.
Results: We study the connections between bioinformatics resources and construct networks of database and software usage patterns, based on resource co-occurrence, that correspond to snapshots of common practice in the bioinformatics community. We apply our approach to pairings of phylogenetics software reported in the literature and argue that these could provide a stepping stone into the identification of scientific best practice.
Availability and implementation: The extracted resource data, the scripts used for network generation and the resulting networks are available at http://bionerds.sourceforge.net/networks/
Contact: robert.stevens@manchester.ac.uk
Prespawning herring distribution in the Irish Celtic Sea between 2005 and 2012
Authors: Volkenandt Mareike , Berrow Simon , O’Connor Ian , Guarini Jean-Marc , O’Donnell Ciaran 
Published: August 2014

Abstract
Knowledge of the spatial distribution of species is fundamental to understanding trophic interactions and ecosystem structure. Intraspecies-specific dynamics and environmental factors shape species distribution within an ecosystem. Distribution patterns and the realized habitat of Atlantic herring (Clupea harengus), a key fish species in the Celtic Sea, were examined using distribution data collected during annual acoustic stock assessment surveys during 2005–2012. Distribution patterns during migration to spawning grounds were analysed using geostatistical methods, including modelling of variograms and comparing the centre of gravity (CG). Distribution patterns were further linked to oceanographic variables collected with the acoustic data to describe habitat. Herring density was greatest inshore along the spawning grounds and lower farther offshore. Herring shoals were clustered and randomly distributed during spawning migration. Variograms of fish densities described the global structure, with high local variability of the same order of magnitude as variability at a regional scale, indicating that no continuous structure can be found within the study area. The CG values showed that the average position of the population was located northeast. The realized habitat for herring encompassed a wide range of temperatures and salinities; therefore, oceanographic features were not a limiting factor for herring distribution during spawning migration. The present study changes the perception of the spatial distribution of the Celtic Sea herring stock from a more continuous distribution to a discrete model, with implications for trophic ecosystem modelling on local scales. Future studies of herring distribution and its influence within the Celtic Sea ecosystem would benefit from using the approach employed in the present study for analysing aggregation patterns.
Proportionality: a valid alternative to correlation for relative data
Authors: Lovell David , Pawlowsky-Glahn Vera , Egozcue Juan José , Marguerat Samuel , Bähler Jürg 
Published: August 2014

AbstractIn the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative—orcompositional—data, differential expression needs careful interpretation, and correlation—a statistical workhorse for analyzing pairwise relationships—is an in-appropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and presentproportionalityas a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statisticΦwhich can be used instead of correlation as the basis of familiar analyses and visualization methods, including co-expression networks and clustered heatmaps.While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.