bluuebunny/crossref_metadata_2025 huggingface dataset:
CIDA: An integrated software for the design, characterisation and global comparison of microarrays
Authors: Khalid Sabah , Khan Mohsin , Symonds Alistair , Fraser Karl , Wang Ping , Liu Xiaohui , Li Suling
Published: December 2017

Microarray technology has had a significant impact in the field of systems biology involving the investigation into the biological systems that regulate human life. Identifying genes of significant interest within any given disease on an individual basis is no doubt time consuming and inefficient when considering the complexity of the human genome. Thus, the genetic profiling of the entire human genome in a single experiment has resulted in microarray technology becoming a widely used experimental tool. However, without the use of tools for several aspects of microarray data analysis the technology is limited. To date, no such tool has been developed that allows the integration of numerous microarray results from different research laboratories as well as the design of customised gene chips in a cost-effective manner. In light of this, we have designed the first integrated and automated software called Chip Integration, Design and Annotation (CIDA) for the cross comparison, design and functional annotation of microarray gene chips. The software provides molecular biologists with the control to cross compare the biological signatures generated from multiple microarray studies, design custom microarray gene chips based on their research requirements and lastly characterise microarray data in the context of immunogenomics. Through the relative comparison of related microarray experiments we have identified 258 genes with common gene expression profiles that are not only upregulated in anergic T cells, but also in cells over-expressing the transcription factor Egr2, that has been identified to play a role in T cell anergy. Using the gene chip design aspect of CIDA we have designed and subsequently fabricate immuno-tolerance gene chips consisting of 1758 genes for further research.

The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk/CIDA/. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchgroups/mi/publications/supplementary/cida
Mapping protein information to disease terminologies
Authors: Mottaz Anaïs , Yip Yum L. , Ruch Patrick , Veuthey Anne-Lise
Published: December 2017

In order to improve the accessibility of genomic and proteomic information to medical researchers, we have developed a procedure to link biological information on proteins involved in diseases to the MeSH and ICD-10 disease terminologies. For this purpose, we took advantage of the manually curated disease annotations in more than 2,000 human protein entries of the UniProt KnowledgeBase. We mapped disease names extracted from the entry comment lines or from the corresponding OMIM entry to the MeSH. The method was assessed on a benchmark set of 200 manually mapped disease comment lines. We obtained a recall of 54% for 91% precision. The same procedure was used to map the more than 3,000 diseases in Swiss-Prot to MeSH with comparable efficiency. Tested on ICD-10, the coverage of the mapped terms was lower, which could be explained by the coarse-grained structure of this terminology for hereditary disease description. The mapping is provided as supplementary material at http://research.isbsib.ch/unimed.
Reactome: An integrated expert model of human molecular processes and access toolkit
Authors: Bono Bernard de , Vastrik Imre , D’Eustachio Peter , Schmidt Esther , Gopinath Gopal , Croft David , Gillespie Marc , Jassal Bijay , Lewis Suzanna , Matthews Lisa , Wu Guanming , Birney Ewan , Stein Lincoln
Published: December 2017

The behaviour of pervasive molecular processes in human biology can be studied through the large-scale modeling of the molecular events that define them. Constructing detailed models of such extent and scope is a considerable undertaking well beyond the reach and capability of individual efforts, due to the range of expertise required. Reactome (http://www.reactome.org) is an open-access project that collaborates with field experts to integrate their pathway knowledge into a single quality-checked human model. This resource dataset is systematically cross-referenced to major molecular and literature databases, and is accessible to the community in a number of well-established formats. Various tools have been developed to facilitate querying and interaction with this content. The salient features of the annotation strategy are discussed here, and examples of pathway and genomic data integration using flexible interfacing methods from the associated toolkit are also presented.
Integrative Visual Analysis of the Effects of Alternative Splicing on Protein Domain Interaction Networks
Authors: Emig Dorothea , Cline Melissa S. , Klein Karsten , Kunert Anne , Mutzel Petra , Lengauer Thomas , Albrecht Mario
Published: December 2017

Proteins and their interactions are essential for the functioning of all organisms and for understanding biological processes. Alternative splicing is an important molecular mechanism for increasing the protein diversity in eukaryotic cells. Splicing events that alter the protein structure and the domain composition can be responsible for the regulation of protein interactions and the functional diversity of different tissues. Discovering the occurrence of splicing events and studying protein isoforms have become feasible using Affymetrix Exon Arrays. Therefore, we have developed the versatile Cytoscape plugin DomainGraph that allows for the visual analysis of protein domain interaction networks and their integration with exon expression data. Protein domains affected by alternative splicing are highlighted and splicing patterns can be compared.
Discovering Distinct Patterns in Gene Expression Profiles
Authors: Teng Li , Chan Laiwan
Published: December 2017

Traditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.
Conservation of the LexA repressor binding site in Deinococcus radiodurans
Authors: Khan Feroz , Singh S. P. , Mishra B. N.
Published: December 2017

The LexA protein is a transcriptional repressor of the bacterial SOS DNA repair system, which comprises a set of DNA repair and cellular survival genes that are induced in response to DNA damage. Its varied DNA binding motifs have been characterized and reported in the Escherichia coli, Bacillus subtilis, rhizobia family members, marine magnetotactic bacterium, Salmonella typhimurium and recently in Mycobacterium tuberculosis and this motifs information has been used in our theoretical analysis to detect its novel regulated genes in radio-resistant Deinococcus radiodurans genome. This bacterium showed presence of SOS-box like consensus sequence in the upstream sequences of 3166 genes with >60% motif score similarity percentage (MSSP) on both strands. Attempts to identify LexA-binding sites and the composition of the putative SOS regulon in D. radiodurans have been unsuccessful so far. To resolve the problem we performed theoretical analysis with modifications on reported data set of genes related to DNA repair (61 genes), stress response (145 genes) and some unusual predicted operons (21 clusters). Expression of some of the predicted SOS-box regulated operon members then was examined through the previously reported microarray data which confirm the expression of only single predicted operon i.e. DRB0143 (AAA superfamily NTPase related to 5-methylcytosine specific restriction enzyme subunit McrB) and DRB0144 (homolog of the McrC subunit of the McrBC restriction modification system). The methodology involved weight matrix construction through CONSENSUS algorithm using information of conserved upstream sequences of eight known genes including dinB, tagC, lexA, recA, uvrB, yneA of B. subtilis while lexA and recA of D. radiodurans through phylogenetic footprinting method and later detection of similar conserved SOS-box like LexA binding motifs through both RSAT & PoSSuMsearch programs. The resultant DNA consensus sequence had highly conserved 14 bp SOS-box like binding site.
WebStruct and VisualStruct: web interfaces and visualization for Structure software implemented in a cluster environment
Authors: Jayashree B. , Rajgopal S. , Hoisington D. , Prasanth V.P. , Chandra S.
Published: December 2017

Structure, is a widely used software tool to investigate population genetic structure with multi-locus genotyping data. The software uses an iterative algorithm to group individuals into “K” clusters, representing possibly K genetically distinct subpopulations. The serial implementation of this programme is processor-intensive even with small datasets. We describe an implementation of the program within a parallel framework. Speedup was achieved by running different replicates and values of K on each node of the cluster. A web-based user-oriented GUI has been implemented in PHP, through which the user can specify input parameters for the programme. The number of processors to be used can be specified in the background command. A web-based visualization tool “Visualstruct”, written in PHP (HTML and Java script embedded), allows for the graphical display of population clusters output from Structure, where each individual may be visualized as a line segment with K colors defining its possible genomic composition with respect to the K genetic sub-populations. The advantage over available programs is in the increased number of individuals that can be visualized. The analyses of real datasets indicate a speedup of up to four, when comparing the speed of execution on clusters of eight processors with the speed of execution on one desktop. The software package is freely available to interested users upon request.
Data Integration for Spatio-Temporal Patterns of Gene Expression of Zebrafish development: the GEMS database
Authors: Belmamoune Mounia , Verbeek Fons J.
Published: December 2017

The Gene Expression Management System (GEMS) is a database system for patterns of gene expression. These patterns result from systematic whole-mount fluorescent in situ hybridization studies on zebrafish embryos. GEMS is an integrative platform that addresses one of the important challenges of developmental biology: how to integrate genetic data that underpin morphological changes during embryogenesis. Our motivation to build this system was by the need to be able to organize and compare multiple patterns of gene expression at tissue level. Integration with other developmental and biomolecular databases will further support our understanding of development. The GEMS operates in concert with a database containing a digital atlas of zebrafish embryo; this digital atlas of zebrafish development has been conceived prior to the expansion of the GEMS. The atlas contains 3D volume models of canonical stages of zebrafish development in which in each volume model element is annotated with an anatomical term. These terms are extracted from a formal anatomical ontology, i.e. the Developmental Anatomy Ontology of Zebrafish (DAOZ). In the GEMS, anatomical terms from this ontology together with terms from the Gene Ontology (GO) are also used to annotate patterns of gene expression and in this manner providing mechanisms for integration and retrieval . The annotations are the glue for integration of patterns of gene expression in GEMS as well as in other biomolecular databases. At the one hand, zebrafish anatomy terminology allows gene expression data within GEMS to be integrated with phenotypical data in the 3D atlas of zebrafish development. At the other hand, GO terms extend GEMS expression patterns integration to a wide range of bioinformatics resources.
BioDWH: A Data Warehouse Kit for Life Science Data Integration
Authors: Töpel Thoralf , Kormeier Benjamin , Klassen Andreas , Hofestädt Ralf
Published: December 2017

This paper presents a novel bioinformatics data warehouse software kit that integrates biological information from multiple public life science data sources into a local database management system. It stands out from other approaches by providing up-to-date integrated knowledge, platform and database independence as well as high usability and customization. This open source software can be used as a general infrastructure for integrative bioinformatics research and development. The advantages of the approach are realized by using a Java-based system architecture and object-relational mapping (ORM) technology. Finally, a practical application of the system is presented within the emerging area of medical bioinformatics to show the usefulness of the approach.

The BioDWH data warehouse software is available for the scientific community at http://sourceforge.net/projects/biodwh/.
Global sequence properties for superfamily prediction: a machine learning approach
Authors: Dobson Richard Jb. , Munroe Patricia B , Caulfield Mark J , Saqi Mansoor
Published: December 2017

Functional annotation of a protein sequence in the absence of experimental data or clear similarity to a sequence of known function is difficult. In this study, a simple set of sequence attributes based on physicochemical and predicted structural characteristics were used as input to machine learning methods. In order to improve performance through increasing the data available for training, a technique of sequence enrichment was explored. These methods were used to predict membership to 24 and 49 large and diverse protein superfamiles from the SCOP database.

We found the best performance was obtained using an enriched training dataset. Accuracies of 66.3% and 55.6% were achieved on datasets comprising 24 and 49 superfamilies with LibSVM and AdaBoostM1 respectively.

The methods used here confirm that domains within superfamilies share global sequence properties. We show machine learning models used to predict categories within the SCOP database can be significantly improved via a simple sequence enrichment step. These approaches can be used to complement profile methods for detecting distant relationships where function is difficult to infer.
Using surveys of Affymetrix GeneChips to study antisense expression
Authors: Sanchez-Graillet Olivia , Stalteri Maria A. , Rowsell Joanna , Upton Graham J.G. , Harrison Andrew P.
Published: December 2017

We have used large surveys of Affymetrix GeneChip HG-U133_Plus_2 data in the public domain to conduct a study of antisense expression across diverse conditions. We derive correlations between groups of probes which map uniquely to the same exon in the antisense direction. When there are no probes assigned to an exon in the sense direction we find that many of the antisense groups fail to detect a coherent block of transcription. We find that only a minority of these groups contain coherent blocks of antisense expression suggesting transcription.

We also derive correlations between groups of probes which map uniquely to the same exon in both sense and antisense direction. In some of these cases the locations of sense probes overlap with the antisense probes, and the sense and antisense probe intensities are correlated with each other. This configuration suggests the existence of a Natural Antisense Transcript (NAT) pair. We find the majority of such NAT pairs detected by GeneChips are formed by a transcript of an established gene and either an EST or an mRNA.

In order to determine the exact antisense regulatory mechanism indicated by the correlation of sense probes with antisense probes, a further investigation is necessary for every particular case of interest. However, the analysis of microarray data has proved to be a good method to reconfirm known NATs, discover new ones, as well as to notice possible problems in the annotation of antisense transcripts.
An integrated dataset for in silico drug discovery
Authors: Cockell Simon J , Weile Jochen , Lord Phillip , Wipat Claire , Andriychenko Dmytro , Pocock Matthew , Wilkinson Darren , Young Malcolm , Wipat Anil
Published: December 2017

Drug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.
Data integration using scanners with SQL output - The Bioscanners project at sourceforge
Authors: Groth Detlef , Hartmann Stefanie , Friemel Martin , Hill Natascha , Müller Stefan , Poustka Albert J. , Panopoulou Georgia
Published: December 2017

There is currently no standardized approach for parsing output that the numerous bioinformatical tools generate. Because the framework approach of the Bio-toolkits has some shortcomings, we searched for alternative approaches. To this end, we evaluated scanner generators for various programming languages with respect to their potential of creating standalone, small, and fast applications that can easily be delivered on any modern and many ancient operating systems. We developed sample applications that generate standard SQL database code and thereby greatly simplify the parsing work of data integration and data analysis. At the sourceforge project page the source code and some binaries for a selection of our applications are freely available at http://bioscanners.sourceforge.net.
An Ontology for Description of Drug Discovery Investigations
Authors: Qi Da , King Ross D. , Hopkins Andrew L. , Bickerton G. Richard J. , Soldatova Larisa N.
Published: December 2017

The paper presents an ontology for the description of Drug Discovery Investigation (DDI). This has been developed through the use of a Robot Scientist “Eve”, and in consultation with industry. DDI aims to define the principle entities and the relations in the research and development phase of the drug discovery pipeline. DDI is highly transferable and extendable due to its adherence to accepted standards, and compliance with existing ontology resources. This enables DDI to be integrated with such related ontologies as the Vaccine Ontology, the Advancing Clinico-Genomic Trials on Cancer Master Ontology, etc. DDI is available at http://purl.org/ddi/wikipedia or http://purl.org/ddi/home
Comparison and Integration of Target Prediction Algorithms for microRNA Studies
Authors: Zhang Yanju , Verbeek Fons J.
Published: December 2017

microRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.
Uncovering the expression patterns of chimeric transcripts using surveys of Affymetrix GeneChips
Authors: Rowsell Joanna , Camargo Renata da Silva , Langdon William B. , Stalteri Maria A. , Harrison Andrew P.
Published: December 2017

Background: A chimeric transcript is a single RNA sequence which results from the transcription of two adjacent genes. Recent studies estimate that at least 4% of tandem human gene pairs may form chimeric transcripts. Affymetrix GeneChip data are used to study the expression patterns of tens of thousands of genes and the probe sequences used in these microarrays can potentially map to exotic RNA sequences such as chimeras.

Results: We have studied human chimeras and investigated their expression patterns using large surveys of Affymetrix microarray data obtained from the Gene Expression Omnibus. We show that for six probe sets, a unique probe mapping to a transcript produced by one of the adjacent genes can be used to identify the expression patterns of readthrough transcripts. Furthermore, unique probes mapping to an intergenic exon present only in the MASK-BP3 chimera can be used directly to study the expression levels of this transcript.

Conclusions: We have attempted to implement a new method for identifying tandem chimerism. In this analysis unambiguous probes are needed to measure run-off transcription and probes that map to intergenic exons are particularly valuable for identifying the expression of chimeras.
CardioVINEdb: a data warehouse approach for integration of life science data in cardiovascular diseases
Authors: Kormeier Benjamin , Hippe Klaus , Töpel Thoralf , Hofestädt Ralf
Published: December 2017

One of the major challenges in bioinfomatics is to integrate and manage data from different sources as well as experimental microarray data and present them in a user-friendly format. Therefore, we present CardioVINEdb, a data warehouse approach developed to interact with and explore life science data. The data warehouse architecture provides a platform independent web interface that can be used with any common web browser. A monitor component controls and updates the data from the different sources to guarantee up-todateness. In addition, the system provides a “static” and “dynamic” visualization component for interactive graphical exploration of the data.
miReg: a resource for microRNA regulation
Authors: Barh Debmalya , Bhat Dattatraya , Viero Cedric
Published: December 2017

MicroRNAs (miRNAs/miRs) are important cellular components that regulate gene expression at posttranscriptional level. Various upstream components regulate miR expression and any deregulation causes disease conditions. Therefore, understanding of miR regulatory network both at upstream and downstream level is crucial and a resource on this aspect will be helpful. Currently available miR databases are mostly related to downstream targets, sequences, or diseases. But as of now, no database is available that provides a complete picture of miR regulation in a specific condition.

Our miR regulation web resource (miReg) is a manually curated one that represents validated upstream regulators (transcription factor, drug, physical, and chemical) along with downstream targets, associated biological process, experimental condition or disease state, up or down regulation of the miR in that condition, and corresponding PubMed references in a graphical and user friendly manner, browseable through 5 browsing options. We have presented exact facts that have been described in the corresponding literature in relation to a given miR, whether it’s a feed-back/feed-forward loop or inhibition/activation. Moreover we have given various links to integrate data and to get a complete picture on any miR listed. Current version (Version 1.0) of miReg contains 47 important human miRs with 295 relations using 190 absolute references. We have also provided an example on usefulness of miReg to establish signalling pathways involved in cardiomyopathy. We believe that miReg will be an essential miRNA knowledge base to research community, with its continuous upgrade and data enrichment.

This HTML based miReg can be accessed from: www.iioab-mireg.webs.com or www.iioab.webs.com/mireg.htm.
From Binary to Multivalued to Continuous Models: The lac Operon as a Case Study
Authors: Franke Raimo , Theis Fabian J. , Klamt Steffen
Published: December 2017

Using the lac operon as a paradigmatic example for a gene regulatory system in prokaryotes, we demonstrate how qualitative knowledge can be initially captured using simple discrete (Boolean) models and then stepwise refined to multivalued logical models and finally to continuous (ODE) models. At all stages, signal transduction and transcriptional regulation is integrated in the model description. We first show the potential benefit of a discrete binary approach and discuss then problems and limitations due to indeterminacy arising in cyclic networks. These limitations can be partially circumvented by using multilevel logic as generalization of the Boolean framework enabling one to formulate a more realistic model of the lac operon. Ultimately a dynamic description is needed to fully appreciate the potential dynamic behavior that can be induced by regulatory feedback loops. As a very promising method we show how the use of multivariate polynomial interpolation allows transformation of the logical network into a system of ordinary differential equations (ODEs), which then enables the analysis of key features of the dynamic behavior.
An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica
Authors: Proß Sabrina , Bachmann Bernhard
Published: December 2017

Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process.

The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab.

The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.
Rapid development of proteomic applications with the AIBench framework
Authors: López-Fernández Hugo , Reboiro-Jato Miguel , Glez-Peña Daniel , Reboredo José R. Méndez , Santos Hugo M. , Carreira Ricardo J. , Capelo-Martínez José L. , Fdez-Riverola Florentino
Published: December 2017

In this paper we present two case studies of Proteomics applications development using the AIBench framework, a Java desktop application framework mainly focused in scientific software development. The applications presented in this work are Decision Peptide- Driven, for rapid and accurate protein quantification, and Bacterial Identification, for Tuberculosis biomarker search and diagnosis. Both tools work with mass spectrometry data, specifically with MALDI-TOF spectra, minimizing the time required to process and analyze the experimental data.
Model Reference Adaptive Scheme for Multi-drug Infusion for Blood Pressure Control
Authors: Enbiya S. , Mahieddine F. , Hossain A.
Published: December 2017

Using multiple interacting drugs to control both the mean arterial pressure (MAP) and cardiac output (CO) of patients with different sensitivity to drugs is a challenging task which this paper attempts to address. A multivariable model reference adaptive control (MRAC) algorithm is developed using a two-input, two-output patient model. The control objective is to maintain the homodynamic variables MAP and CO at the normal values by simultaneously administering two drugs; sodium nitroprusside (SNP) and dopamine (DPM). Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters.
Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment
Authors: Silva Fernando José Mateus da , Pérez Juan Manuel Sánchez , Pulido Juan Antonio Gómez , Rodríguez Miguel A. Vega
Published: December 2017

Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an Evolutionary Algorithm which uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA.We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.
Evaluating the effect of unbalanced data in biomedical document classification
Authors: Laza Rosalía , Pavón Reyes , Reboiro-Jato Miguel , Fdez-Riverola Florentino
Published: December 2017

Nowadays, document classification has become an interesting research field. Partly, this is due to the increasing availability of biomedical information in digital form which is necessary to catalogue and organize. In this context, machine learning techniques are usually applied to text classification by using a general inductive process that automatically builds a text classifier from a set of pre-classified documents. Related with this domain, imbalanced data is a well-known problem in many practical applications of knowledge discovery and its effects on the performance of standard classifiers are remarkable. In this paper, we investigate the application of a Bayesian Network (BN) model for the triage of documents, which are represented by the association of different MeSH terms. Our results show that BNs are adequate for describing conditional independencies between MeSH terms and that MeSH ontology is a valuable resource for representing Medline documents at different abstraction levels. Moreover, we perform an extensive experimental evaluation to investigate if the classification of Medline documents using a BN classifier poses additional challenges when dealing with class-imbalanced prediction. The evaluation involves two methods, under-sampling and cost-sensitive learning. We conclude that BN classifier is sensitive to both balancing strategies and existing techniques can improve its overall performance.
Predicting protein distance maps according to physicochemical properties
Authors: Cortés Gualberto Asencio , Aguilar-Ruiz Jesús A.
Published: December 2017

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein con- sists in determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, we used a set of 30 physicochemical amino acid properties selected from the AAindex database. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. We performed several preliminary analysis of the protein physicochemical data distributions using 3D surfaces. Three main pattern types were found in 3D surfaces, thus it is possible to extract rules in order to predict distances between amino acids according to their physicochemical properties. We performed an experimental validation of our method using five non-homologous protein sets and we showed the generality of this method and its prediction quality using the amino acid properties considered. Finally, we included a study of the algorithm efficiency according to the number of most similar fragments considered and we notably improved the precision with the studied proteins sets.
The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks
Authors: Swainston Neil , Smallbone Kieran , Mendes Pedro , Kell Douglas B. , Paton Norman W.
Published: December 2017

The generation and use of metabolic network reconstructions has increased over recent years. The development of such reconstructions has typically involved a time-consuming, manual process. Recent work has shown that steps undertaken in reconstructing such metabolic networks are amenable to automation.

The SuBliMinaL Toolbox (http://www.mcisb.org/subliminal/) facilitates the reconstruction process by providing a number of independent modules to perform common tasks, such as generating draft reconstructions, determining metabolite protonation state, mass and charge balancing reactions, suggesting intracellular compartmentalisation, adding transport reactions and a biomass function, and formatting the reconstruction to be used in third-party analysis packages. The individual modules manipulate reconstructions encoded in Systems Biology Markup Language (SBML), and can be chained to generate a reconstruction pipeline, or used individually during a manual curation process.

This work describes the individual modules themselves, and a study in which the modules were used to develop a metabolic reconstruction of Saccharomyces cerevisiae from the existing data resources KEGG and MetaCyc. The automatically generated reconstruction is analysed for blocked reactions, and suggestions for future improvements to the toolbox are discussed.
METU-SNP: An Integrated Software System for SNPComplex Disease Association Analysis
Authors: Üstünkar Gürkan , Son Yeşim Aydın
Published: December 2017

Recently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual’s genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries
Integrating Protein-Protein Interaction Networks with Gene- Gene Co-Expression Networks improves Gene Signatures for Classifying Breast Cancer Metastasis
Authors: Akker Erik van den , Verbruggen Bas , Heijmans Bas , Beekman Marian , Kok Joost , Slagboom Eline , Reinders Marcel
Published: December 2017

Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.
On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data
Authors: Trede Dennis , Kobarg Jan Hendrik , Oetjen Janina , Thiele Herbert , Maass Peter , Alexandrov Theodore
Published: December 2017

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies.

A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.
Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights
Authors: Gong Xiu-Jun , Yu Hua , Yang Chun-Bai , Li Yuan-Fang
Published: December 2017

The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS) and housekeeping (HK) genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA) and Hypergeometric Analysis (HA) against three biological ontologies: Gene Ontology (GO), KEGG pathways and Disease Ontology (DO) respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease) are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM)” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC)” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO) for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.
Detection of Cardiac Geometry via Difference Intensity of Echocardiogram Images
Authors: Mohd Adib Mohd Azrul Hisham , Yusof Mohd Fadhlan , Zulkifli Ahmad , Nur Hazreen Mohd Hasni
Published: December 2017

Echocardiogram is an ultrasound image of the heart that demonstrates the size, motion and composition of cardiac structures and is also used to diagnose various abnormalities of the heart including abnormal chamber size, shape and congenital heart disease. Echocardiography provides important morphological and functional details of the heart. Most of the presented automatic cardiac disease recognition systems that use echocardiograms based on defective anatomical region detection. In this paper we present a simple technique for cardiac geometry detection via echocardiogram images which conquer these borders and exploits cues from cardiac structure. To demonstrate the effectiveness of this technique, we present results for cardiac geometry detection through difference intensity of echocardiogram images. We have developed a simple program code for the prediction of cardiac geometry using difference intensity of echocardiogram images. With this code, users can generate node or point for detection of cardiac geometry as ventricle and atrium in size, shape and location.
On the parameter optimization of Support Vector Machines for binary classification
Authors: Gaspar Paulo , Carbonell Jaime , Oliveira José Luís
Published: December 2017

Classifying biological data is a common task in the biomedical context. Predicting the class of new, unknown information allows researchers to gain insight and make decisions based on the available data. Also, using classification methods often implies choosing the best parameters to obtain optimal class separation, and the number of parameters might be large in biological datasets.

Support Vector Machines provide a well-established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set and the tuning of hyper-parameters. Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive.

In this paper we review the strategies that are used to improve the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels.
Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data
Authors: Cai Jinlu , Keen Henry L. , Sigmund Curt D. , Casavant Thomas L.
Published: December 2017

Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem.

As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective.

Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.
Modular analysis of gene networks by linear temporal logic
Authors: Ito Sohei , Ichinose Takuma , Shimakawa Masaya , Izumi Naoko , Hagihara Shigeki , Yonezaki Naoki
Published: December 2017

Despite a lot of advances in biology and genomics, it is still difficult to utilise such valuable knowledge and information to understand and analyse large biological systems due to high computational complexity. In this paper we propose a modular method with which from several small network analyses we analyse a large network by integrating them. This method is based on the qualitative framework proposed by authors in which an analysis of gene networks is reduced to checking satisfiability of linear temporal logic formulae. The problem of linear temporal logic satisfiability checking needs exponential time in the size of a formula. Thus it is difficult to analyse large networks directly in this method since the size of a formula grows linearly to the size of a network. The modular method alleviates this computational difficulty. We show some experimental results and see how we benefit from the modular analysis method.
Community structure of non-coding RNA interaction network
Authors: Nacher Jose C.
Published: December 2017

Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between noncoding RNAs (ncRNAs) and proteins related bio-macrolecules (PRMs) using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell.