bluuebunny/crossref_metadata_2025 huggingface dataset:
Production of calla lily grown in an NFT system
Authors: Landgraf Paulo Roberto Correa , Marques Douglas José , Montovani José Ricardo , Da Silva Adriano Botolotti , De Oliveira Paiva Patrícia Duarte , De Almeida Elka Fabiana Aparecida , Da Silva Rodrigo Sérgio
Published: December 2017

The objective of this study was to evaluate the production of calla lily in an NFT system. The experiment was carried out in a greenhouse, using a 2 x 2 factorial scheme in a completely randomized design (CRD), with fifteen replications. The treatments were a combination of two hydroponic profiles (100 and 150 mm of height) and two nutrient solutions. Calla lily plantlets obtained from rhizome buds in trays containing nutrient solution, were transferred to a laminar flow of nutrients, and the experiment lasted 12 months. The height of stems and inflorescences were evaluated, as well as the length and diameter of the inflorescence, the number of flowers per plant and number of flowers per m2 . Growing calla lily plants in an NFT system is feasible. The nutrient solution with the highest concentration of nutrients, particularly N and K, and the profile of 150 mm, are the most suitable for the production of calla lily as a cut flower in a laminar flow of nutrients.
Superposition of p-superharmonic functions
Authors: Brustad Karl K.
Published: December 2017

The dominative p-Laplace operator is introduced. This operator is a relative to the p-Laplacian, but with the distinguishing property of being sublinear. It explains the superposition principle in the p-Laplace equation.
Rethinking Kant’s Debts to Rousseau
Authors: Hanley Ryan Patrick
Published: December 2017

That Kant owed much to Rousseau has long been known. Yet some of Kant’s most significant debts to Rousseau still await appreciation. This paper offers an examination of two key understudied writings from the 1760s in order to provide a more comprehensive account of Kant’s debts to Rousseau. In focusing on these texts, it particularly aims to supplement the existing accounts of Kant’s debts to Rousseau which have largely focused on concepts of human dignity and human equality or on concepts of gender and gender relations, and to demonstrate the degree to which Kant read Rousseau as a theorist of epistemic development. In so doing it offers a reconsideration of Kant’s own philosophical development, and a reconsideration of the philosophical significance of Rousseau’s epistemology.
A Comparative Stratigraphy of Cilicia
Authors: Novák Mirko , D’Agata Anna Lucia , Caneva Isabella , Eslick Christine , Gates Charles , Gates Marie-Henriette , Girginer K. Serdar , Oyman-Girginer Özlem , Jean Éric , Köroğlu Gülgün , Kozal Ekin , Kulemann-Ossen Sabina , Lehmann Gunnar , Özyar Aslı , Ozaydın Tülay , Postgate J. Nicholas , Şahin Fatma , Ünlü Elif , Yağcı Remzi , Meier Deniz Yaşin
Published: December 2017

This article presents a preliminary comparative stratigraphy of excavated sites in Plain Cilicia and one in Rough Cilicia. It is the outcome of three workshops held in 2014, 2015 and 2017. Plain Cilicia at the junction of Anatolia, Syro-Mesopotamia and Cyprus is one of the most fertile regions of the Ancient Near East. In recent years, archaeological research in the region has intensified, re-opening questions of chronology. The comparative stratigraphy discussed in the workshops is presented here in form of a gazetteer of the participating sites and a chart. This is to be understood as a first step towards a more comprehensive chronology.
Determination in the Anatolian Hieroglyphic Script of the Empire and Transitional Period
Authors: Payne Annick
Published: December 2017

The Anatolian Hieroglyphic script is a mixed writing system which contains both phonetic and semantographic signs. The latter may be used in the function of logogram and/or determinative. A dedicated study of the script’s determinatives has so far not been undertaken but promises insight into structures of mental organization and script development. Because of its pictorial character, individual signs can occasionally be shown to act in a dual capacity, as icons and signs of writing. This article forms part one of a diachronic study of the determinative system, and adresses the period 13
Starb Philo von Larisa im Alter von 63 Jahren?
Authors: Fleischer Kilian
Published: December 2017

New readings in Philodernus' Index Academicorum (PHerc. 1021, col. 33, 17- 19 and 42-44) show that the papyrus reports the age of Philo of Larisa at his death as 63, considerably younger than hitherto assumed. The validity of this new information and possible consequences for Philo' s biography are discussed.
The UGT1A1*28 gene variant predicts long-term mortality in patients undergoing coronary angiography
Authors: Zulus Barbara , Grünbacher Gerda , Kleber Marcus E. , März Winfried , Renner Wilfried
Published: December 2017

Background:

Uridine diphosphate glycosyltransferases 1A1 (UGT1A1) plays an essential role in detoxification and excretion of several endogenous and exogenous compounds. A functional polymorphism in the promoter of the UGT1A1 gene (TA repeat insertion, UGT1A1*28, rs3064744) has been associated with reduced UGT1A1 enzyme activity. The purpose of the present study was to investigate the role of UGT1A1 genotypes in mortality.

Methods:

UGT1A1 genotypes as well as baseline plasma bilirubin levels were analyzed in participants of the Ludwigshafen Risk and Cardiovascular Health study (n=3316). UGT1A1*28 genotypes were determined on an ABI PRISM 3730 genetic analyzer.

Results:

As expected, UGT1A1 genotypes were associated with baseline bilirubin levels (*1/*1 genotype: 9.1±4.6 µmol/L; *1/*28 genotype: 10.8±5.3; *28/*28: 16.9±9.2; p<0.001). During a median follow-up of 10.4 years, 995 subjects (30.0%) died. In a multivariate regression analysis adjusting for age, sex, smoking, type 2 diabetes, dyslipidemia, alanine aminotransferase (ALT) levels and bilirubin levels, the UGT1A1*28 variant predicted lower overall mortality (hazard ratio [HR], 0.86; 95% confidence interval [CI], 0.78–0.95; p=0.003). Contrary to expected, higher baseline bilirubin levels predicted increased mortality (HR, 1.014; 95% CI, 1.002–1.025; p=0.019).

Conclusions:

The UGT1A1*28 gene variant is associated with lower mortality rates. The protective effect of the UGT1A1*28 variant likely includes mechanism other than bilirubin metabolism.
Indexes of the erythropoietin level in the blood plasma of chronic heart failure patients with anemia
Authors: Zahidova Kamala Kh.
Published: December 2017

Background:

Anemia aggravates the disease course and the survival rate of chronic heart failure (CHF) patients. The purpose of the study was to investigate the level of erythropoietin (EPO) in CHF patients with anemic syndrome, with the aim to more accurately assess the severity of the disease and its treatment, depending on the anemia degree.

Methods:

Patients with ischemic CHF of I–IV functional class (FC) with and without anemia were examined (total number of patients=208, patients with anemia=174). The EPO was determined using the enzyme-linked immunosorbent assay. Before treatment, the patients underwent the following medical therapy: angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, long-acting nitrates, diuretics, digoxin, and beta-blockers at individual doses. Depending on the plasma EPO level, the CHF patients with anemia were divided into four randomized groups in terms of treatment.

Results:

Normal erythropoietinemia was found in 36.2% of the CHF patients with anemic syndrome (I–III FC), hypoerythropoietinemia in 44.8% (III–IV FC), and hypererythropoietinemia in 18.96% (III–V FC). The EPO level in the blood plasma of the patients with I–II FC CHF with hypoerythropoietinemia, who were treated with methoxy polyethylene glycol-epoetin β (MEB), increased by 2.2 times. Combination therapy with disease-modifying drugs and MEB led to a significant increase in the plasma EPO level in the CHF patients with hypoerythropoietinemia.

Conclusions:

It was shown that the EPO level in patients with CHF and anemia did not always drop. Hypererythropoietinemia in patients with CHF and anemia leads to an unfavorable treatment prediction. This necessitates the investigation of the EPO level in all patients with CHF before and after treatment, with the aim of correcting the anemic syndrome. The research showed that the combined therapy of patients with CHF and anemia using MEB medication and iron with regard to the EPO level in the blood plasma improved their overall physical condition, reduced heart failure symptoms and hospitalization frequency, and demonstrated a clear tendency to reduce the general mortality rate.
Ontology-Assisted Database Integration to Support Natural Language Processing and Biomedical Data-mining
Authors: Verschelde Jean-Luc , Dos Santos Mariana Casella , Deray Tom , Smith Barry , Ceusters Werner
Published: December 2017

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language.
Genlight: Interactive high-throughput sequence analysis and comparative genomics
Authors: Beckstette Michael , Mailänder Jens T. , Marhöfer Richard J. , Sczyrba Alexander , Ohlebusch Enno , Giegerich Robert , Selzer Paul M.
Published: December 2017

With rising numbers of fully sequenced genomes the importance of comparative genomics is constantly increasing. Although several software systems for genome comparison analyses do exist, their functionality and flexibility is still limited, compared to the manifold possible applications. Therefore, we developed Genlight(http://piranha.techfak.uni-bielefeld.de.), a Client/Server based program suite for large scale sequence analysis and comparative genomics. Genlight uses the object relational database system PostgreSQL together with a state of the art data representation and a distributed execution approach for large scale analysis tasks. The system includes a wide variety of comparison and sequence manipulation methods and supports the management of nucleotide sequences as well as protein sequences. The comparison methods are complemented by a large variety of visualization methods for the assessment of the generated results. In order to demonstrate the suitability of the system for the treatment of biological questions, Genlight was used to identify potential drug and vaccine targets of the pathogen Helicobacter pylori.
BASIS: an internet resource for network modelling
Authors: Gillespie Colin S. , Wilkinson Darren J. , Shanley Daryl P. , Proctor Carole J. , Boys Richard J. , Kirkwood Thomas B.L.
Published: December 2017

There is a growing realisation that complex biological processes cannot be understood through the application of ever more reductionist experimental programs alone. Recognising this, we have a constructed a flexible web-service based modelling system called BASIS (Biology of Ageing e-Science Integration and Simulation), which facilitates model construction and development. In particular it allows users to store, share and simulate their models. The system is accessed through web-services using any language (e.g. Python or Java) or under any operating system (e.g. Linux or Windows).
Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments
Authors: Martinez Ricardo , Pasquier Nicolas , Collard Martine , Pasquier Claude , Lopez-Perez Lucero
Published: December 2017

Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to wellknown microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.
Combination of a data warehouse concept with web services for the establishment of the Pseudomonas systems biology database SYSTOMONAS
Authors: Choi Claudia , Münch Richard , Bunk Boyke , Barthelmes Jens , Ebeling Christian , Schomburg Dietmar , Schobert Max , Jahn Dieter
Published: December 2017

Systems biology requires the integration of data from various sources and their combined interpretation using different bioinformatics tools. Integration of different biological databases, however, is often problematic due to their semantic and structural diversity. Moreover, necessary continuous updates of both the structure and content of a database provide further challenges for an integration process. We established the novel database SYSTOMONAS for SYSTems biology of pseudOMONAS by integrating heterogeneous data from highly different external resources including BioCyc, BRENDA, ENZYME, Pseudomonas Genome Database v2, KEGG, and PRODORIC. For this purpose we combined a data warehouse concept with the advantages of web services. This hybrid approach benefits from the fast performance and data consistency provided by the data warehouse system and from the up-to-dateness ensured by use of dynamic web services. The data warehouse part is realized by ETL processes (Extract, Transform, Load), during which data are checked for consistency and standardized to ensure their integrity. While accessing SYSTOMONAS via the internet, parts of the data warehouse content are dynamically enriched using the web service part of the system via SOAP (originally for Simple Object Access Protocol) interfaces with BRENDA, KEGG and PRODORIC. SYSTOMONAS is designed to integrate in-house experimental high-throughput data with up-to-date information available in the mentioned public databases. SYSTOMONAS also serves as a repository for the prediction of metabolic and regulatory networks. SYSTOMONAS is accessible at http://www.systomonas.de.
SABIO-RK: A data warehouse for biochemical reactions and their kinetics
Authors: Krebs Olga , Golebiewski Martin , Kania Renate , Mir Saqib , Saric Jasmin , Weidemann Andreas , Wittig Ulrike , Rojas Isabel
Published: December 2017

Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.
A Two-Step Clustering for 3-D Gene Expression Data Reveals the Main Features of the Arabidopsis Stress Response
Authors: Strauch Martin , Supper Jochen , Spieth Christian , Wanke Dierk , Kilian Joachim , Harter Klaus , Zell Andreas
Published: December 2017

We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-means clustering on one condition is performed, which is subsequently used in the second step as a seed for the clustering of the remaining conditions. To validate the significance of the obtained modules, we performed a permutation analysis and determined a null hypothesis to compare the module scores against, providing a p-value for each module. Significant modules were mapped to the Gene Ontology (GO) in order to determine the participating biological processes.

As a result, we isolated modules showing high significance with respect to the p-values obtained by permutation analysis and GO mapping. In these modules we identified a number of genes that are either part of a general stress response with similar characteristics under different conditions (coherent modules), or part of a more specific stress response to a single stress condition (single response modules). We also found genes clustering within several conditions, which are, however, not part of a coherent module. These genes have a distinct temporal response under each condition. We call the modules they are contained in individual response modules (IR).
Achieving k-anonymity in DataMarts used for gene expressions exploitation
Authors: Stark Konrad , Eder Johann , Zatloukal Kurt
Published: December 2017

Gene expression profiling is a sophisticated method to discover differences in activation patterns of genes between different patient collectives. By reasonably defining patient groups from a medical point of view, subsequent gene expression analysis may reveal disease-related gene expression patterns that are applicable for tumor markers and pharmacological target identification. When releasing patient-specific data for medical studies privacy protection has to be guaranteed for ethical and legal reasons. k-anonymisation may be used to generate a sufficient number of k data twins in order to ensure that sensitive data used in analyses is protected from being linked to individuals. We use an adapted concept of k-anonymity for distributed data sources and include various customisation parameters in the anonymisation process to guarantee that the transformed data is still applicable for further processing. We present a real-world medical-relevant use case and show how the related data is materialised, anonymised, and released in a data mart for testing the related hypotheses.
A High-Level Petri Net Framework for Genetic Regulatory Networks
Authors: Banks Richard , Steggles L. Jason
Published: December 2017

To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.
Exploring PSI-MI XML Collections Using DescribeX
Authors: Samavi Reza , Consens Mariano , Khatchadourian Shahan , Topaloglou Thodoros
Published: December 2017

PSI-MI has been endorsed by the protein informatics community as a standard XML data exchange format for protein-protein interaction datasets. While many public databases support the standard, there is a degree of heterogeneity in the way the proposed XML schema is interpreted and instantiated by different data providers. Analysis of schema instantiation in large collections of XML data is a challenging task that is unsupported by existing tools.

In this study we use DescribeX, a novel visualization technique of (semi-)structured XML formats, to quantitatively and qualitatively analyze PSI-MI XML collections at the instance level with the goal of gaining insights about schema usage and to study specific questions such as: adequacy of controlled vocabularies, detection of common instance patterns, and evolution of different data collections. Our analysis shows DescribeX enhances understanding the instance-level structure of PSI-MI data sources and is a useful tool for standards designers, software developers, and PSI-MI data providers.
Mining for Single Nucleotide Polymorphisms in Expressed Sequence Tags of European Sea Bass
Authors: Souche E. L. , Hellemans B. , Van Houdt J. K. J. , Canario A. , Klages S. , Reinhardt R. , Volckaert F. A. M.
Published: December 2017

As a multitude of sequence data are published, discovering polymorphisms bioinformatically becomes a valid option. In silico Single Nucleotide Polymorphism (SNP) detection is based on the analysis of multiple alignments. Each column of an alignment is considered a slice containing one base of every sequence aligned. If a mismatch is detected, the slice is further analysed and the mismatch may be reported as a candidate SNP.

About 30,000 Expressed Sequence Tags (ESTs) of the fish European sea bass have been sequenced and processed. Since ESTs are redundant, they provide a resource for in silico SNP discovery. To prevent the detection of sequencing errors, a redundancy of two is chosen in order for a mismatch to be considered a candidate SNP. Among the various tools available to detect candidate SNPs, three software packages were tested: SNPServer, PolyBayes and PolyFreq. Candidate SNPs were validated in the laboratory by cloning and sequencing. From preliminary results PolyFreq outperforms both PolyBayes and SNPServer in terms of positive predictive value and SNPServer is the most sensitive tool. PolyFreq and SNPServer non-default identify respectively the fewest and highest number of candidates. Considering candidates detected by several tools seems to enhance both positive predictive value and sensitivity. Out of the 69 loci sequenced, only four were monomorphic, leading to a total of 91.3% polymorphic loci. Randomly chosen contigs will be sequenced to know whether SNP discovery tools tend to predict polymorphic fragments. Polymorphisms will be mapped, used for selection in aquaculture and the study of adaptation in natural populations.
Prediction of protein-protein interactions using one-class classification methods and integrating diverse biological data
Authors: Reyes José A. , Gilbert David
Published: December 2017

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results.

Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature.
An Unusual Distribution of 6-nt Sequences Near The Transcription Start Site
Authors: Mitra Chanchal K. , Milanesi Luciano
Published: December 2017

A new look at the transcription start is presented in which we can see transcription factors binding to both sides of the TSS as an essential requirement. Naturally the factor binding to the downstream region must be removed so that transcription process can continue. The presence of a number of distinct transcription factors also can be used to explain selective activation of various genes. The transcription start site by itself plays only a minor role in the whole process. We also suggest that mutations close to the TSS on the coding side can be fatal even if preserves the codon table.
Visual Comparison of Multiple Gene Expression Datasets in a Genomic Context
Authors: Borowski Krzysztof , Soh Jung , Sensen Christoph W.
Published: December 2017

The need for novel methods of visualizing microarray data is growing. New perspectives are beneficial to finding patterns in expression data. The Bluejay genome browser provides an integrative way of visualizing gene expression datasets in a genomic context. We have now developed the functionality to display multiple microarray datasets simultaneously in Bluejay, in order to provide researchers with a comprehensive view of their datasets linked to a graphical representation of gene function. This will enable biologists to obtain valuable insights on expression patterns, by allowing them to analyze the expression values in relation to the gene locations as well as to compare expression profiles of related genomes or of di erent experiments for the same genome.
Integration of -omics data and networks for biomedical research with VANTED
Authors: Klukas Christian , Schreiber Falk
Published: December 2017

Increasingly, research focus in the fields of biology and medicine moves from the investigation of single phenomena to the analysis of complex cause and effect relationships. The clarification of complicated relationships requires the consideration of different domains, such as gene expression, protein, and metabolite data. Furthermore, it is often sensible not to analyze the collected data in isolation, but to consider the context of relevant biological networks. In this paper newly developed functionalities of the VANTED system are presented. They allow users from medicine and biology to interactively structure extensive experimental data, to filter, to evaluate, and to visualize the data and the analysis results in the context of biological networks and classification hierarchies.
Quality controls in integrative approaches to detect errors and inconsistencies in biological databases
Authors: Ghisalberti Giorgio , Masseroli Marco , Tettamanti Luca
Published: December 2017

Numerous biomolecular data are available, but they are scattered in many databases and only some of them are curated by experts. Most available data are computationally derived and include errors and inconsistencies. Effective use of available data in order to derive new knowledge hence requires data integration and quality improvement. Many approaches for data integration have been proposed. Data warehousing seams to be the most adequate when comprehensive analysis of integrated data is required. This makes it the most suitable also to implement comprehensive quality controls on integrated data. We previously developed GFINDer (http://www.bioinformatics.polimi.it/GFINDer/), a web system that supports scientists in effectively using available information. It allows comprehensive statistical analysis and mining of functional and phenotypic annotations of gene lists, such as those identified by high-throughput biomolecular experiments. GFINDer backend is composed of a multi-organism genomic and proteomic data warehouse (GPDW). Within the GPDW, several controlled terminologies and ontologies, which describe gene and gene product related biomolecular processes, functions and phenotypes, are imported and integrated, together with their associations with genes and proteins of several organisms. In order to ease maintaining updated the GPDW and to ensure the best possible quality of data integrated in subsequent updating of the data warehouse, we developed several automatic procedures. Within them, we implemented numerous data quality control techniques to test the integrated data for a variety of possible errors and inconsistencies. Among other features, the implemented controls check data structure and completeness, ontological data consistency, ID format and evolution, unexpected data quantification values, and consistency of data from single and multiple sources. We use the implemented controls to analyze the quality of data available from several different biological databases and integrated in the GFINDer data warehouse. By doing so, we identified in these data a variety of different types of errors and inconsistencies; this enables us to ensure good quality of the data in the GFINDer data warehouse. We reported all identified data errors and inconsistencies to the curators of the original databases from where the data were retrieved, who mainly corrected them in subsequent updating of the original database. This contributed to improve the quality of the data available, in the original databases, to the whole scientific community.
An evaluation of the performance of three semantic background knowledge sources in comparative anatomy
Authors: Ophuizen Ernest A.A. van , Leunissen Jack A.M.
Published: December 2017

In this paper we evaluate the performance and usefulness of three semantic background knowledge sources for predicting synonymous anatomical terms across species boundaries. The reference sources under evaluation are UMLS, FMA-OBO and WordNet, which are applied to the anatomical ontologies of mouse and zebrafish. Our results show that the use of specialized knowledge sources leads to highly accurate predictions, verified through complete manual curation, which can be further improved by combining multiple of said sources. We argue that these three references complement each other in terms of granularity and specificity. From our results we conclude that these references can be used to create reliable ontology mappings with minimal human supervision.
Mining and Analysing Spatio-Temporal Patterns of Gene Expression in An Integrative database Framework
Authors: Belmamoune M. , Potikanond D. , Verbeek Fons J.
Published: December 2017

Mining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results. In our particular case we describe a solution that is found in the extension of the Gene Expression Management System (GEMS), i.e. an integrative framework for spatio-temporal organization of gene expression patterns of zebrafish to a framework supporting data mining, data analysis and patterns interpretation As a proof of principle, the GEMS has been equipped with data mining functionality suitable for spatio-temporal tracking, thereby generating added value to the submission of data for data mining and analysis. The analysis of the genetic networks is based on the availability of domain ontologies which dynamically provides meaning to the discovered patterns of gene expression data. Combination of data mining with the already presently available capabilities of GEMS will significantly augment current data processing and functional analysis strategies
SNPRanker: a tool for identification and scoring of SNPs associated to target genes
Authors: Calabria Andrea , Mosca Ettore , Viti Federica , Merelli Ivan , Milanesi Luciano
Published: December 2017

The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker. An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.
Bioinformatics Strategies in Life Sciences: From Data Processing and Data Warehousing to Biological Knowledge Extraction
Authors: Thiele Herbert , Glandorf Jörg , Hufnagel Peter
Published: December 2017

With the large variety of Proteomics workflows, as well as the large variety of instruments and data-analysis software available, researchers today face major challenges validating and comparing their Proteomics data. Here we present a new generation of the ProteinScape
MADMAX – Management and analysis database for multiple ~omics experiments
Authors: Lin Ke , Kools Harrie , de Groot Philip J. , Gavai Anand K. , Basnet Ram K. , Cheng Feng , Wu Jian , Wang Xiaowu , Lommen Arjen , Hooiveld Guido J. E. J. , Bonnema Guusje , Visser Richard G. F. , Muller Michael R. , Leunissen Jack A. M.
Published: December 2017

The rapid increase of ~omics datasets generated by microarray, mass spectrometry and next generation sequencing technologies requires an integrated platform that can combine results from different ~omics datasets to provide novel insights in the understanding of biological systems. MADMAX is designed to provide a solution for storage and analysis of complex ~omics datasets. In addition, analysis results (such as lists of genes) can be merged to reveal candidate genes supported by all datasets. The system constitutes an ISA-Tab compliant LIMS part, which is linked to the different analysis pipelines. A pilot study of different type of ~omics data in Brassica rapa demonstrates the possible use of MADMAX. The web-based user interface provides easy access to data and analysis tools on top of the database.
A generic organ based ontology system, applied to vertebrate heart anatomy, development and physiology
Authors: Bertens Laura M.F. , Slob Joris , Verbeek Fons J.
Published: December 2017

We present a novel approach to modelling biological information using ontologies. The system interlinks three ontologies, comprising anatomical, developmental and taxonomical information, and includes instances of structures for different species. The framework is constructed for comparative analyses in the field of evolutionary development. We have applied the approach to the vertebrate heart and present four case studies of the functionality of the system, focusing on cross-species comparisons, developmental studies, physiological studies and 3D visualisation.
Improving imbalanced scientific text classification using sampling strategies and dictionaries
Authors: Borrajo L. , Romero R. , Iglesias E. L. , Marey C. M. Redondo
Published: December 2017

Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation.

Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when they are applied to search on the PubMed scientific database.

Another purpose of this paper is to study the use of dictionaries in the classification of biomedical texts. Experiments are conducted with three different dictionaries (BioCreative, NLPBA, and an ad-hoc subset of the UniProt database named Protein) using the mentioned classifiers and sampling strategies.

Best results were obtained with NLPBA and Protein dictionaries and the SVM classifier using the Subsampling balancing technique. These results were compared with those ob- tained by other authors using the TREC Genomics 2005 public corpus.
A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies
Authors: Camacho Rui , Pereira Max , Costa Vítor Santos , Fonseca Nuno A. , Adriano Carlos , Simões Carlos J. V. , Brito Rui M. M.
Published: December 2017

It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current “target-rich, lead-poor” scenario.

Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal - iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms.
Automatic extraction of microorganisms and their habitats from free text using text mining workflows
Authors: Kolluru BalaKrishna , Nakjang Sirintra , Hirt Robert P. , Wipat Anil , Ananiadou Sophia
Published: December 2017

In this paper we illustrate the usage of text mining workflows to automatically extract instances of microorganisms and their habitats from free text; these entries can then be curated and added to different databases. To this end, we use a Conditional Random Field (CRF) based classifier, as part of the workflows, to extract the mention of microorganisms, habitats and the inter-relation between organisms and their habitats.

Results indicate a good performance for extraction of microorganisms and the relation extraction aspects of the task (with a precision of over 80%), while habitat recognition is only moderate (a precision of about 65%). We also conjecture that pdf-to-text conversion can be quite noisy and this implicitly affects any sentence-based relation extraction algorithms.
Analysis of high-throughput plant image data with the information system IAP
Authors: Klukas Christian , Pape Jean-Michel , Entzian Alexander
Published: December 2017

This work presents a sophisticated information system, the Integrated Analysis Platform (IAP), an approach supporting large-scale image analysis for different species and imaging systems. In its current form, IAP supports the investigation of Maize, Barley and Arabidopsis plants based on images obtained in different spectra.

Several components of the IAP system, which are described in this work, cover the complete end-to-end pipeline, starting with the image transfer from the imaging infrastructure, (grid distributed) image analysis, data management for raw data and analysis results, to the automated generation of experiment reports.
Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression
Authors: Calvo-Dmgz D. , Gálvez J. F. , Glez-Peña D. , Gómez-Meire S. , Fdez-Riverola F.
Published: December 2017

DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.
Construction of coffee transcriptome networks based on gene annotation semantics
Authors: Castillo Luis F. , Galeano Narmer , Isaza Gustavo A. , Gaitan Alvaro
Published: December 2017

Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.
Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series
Authors: Carreiro André V. , Ferreira Artur J. , Figueiredo Mário A. T. , Madeira Sara C.
Published: December 2017

Biclustering has been recognized as a remarkably effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms, essential to understanding complex biomedical processes, such as disease progression and drug response. In this work, we propose a classification approach based on meta-biclusters (a set of similar biclusters) applied to prognostic prediction. We use real clinical expression time series to predict the response of patients with multiple sclerosis to treatment with Interferon-β. As compared to previous approaches, the main advantages of this strategy are the interpretability of the results and the reduction of data dimensionality, due to biclustering. This would allow the identification of the genes and time points which are most promising for explaining different types of response profiles, according to clinical knowledge. We assess the impact of different unsupervised and supervised discretization techniques on the classification accuracy. The experimental results show that, in many cases, the use of these discretization methods improves the classification accuracy, as compared to the use of the original features.
Predicting breast cancer chemotherapeutic response using a novel tool for microarray data analysis
Authors: Cheng Jie , Greshock Joel , Painter Jeffery , Lin Xiwu , Lee Kwan , Zheng Shu , Menius Alan
Published: December 2017

We developed a novel tool for microarray data analysis that can parsimoniously discover highly predictive genes by finding the optimal trade off between fold change and t-test p value through rigorous cross validation. In addition to find a small set of highly predictive genes, the tool also has a procedure that recursively discovers and removes predictive genes from the dataset until no such genes can be found. We applied our tool to a public breast cancer dataset with the goal to discover genes that can predict patient’s response to a preoperative chemotherapy. The results show that estrogen receptor (ER) gene is the most important gene to predict chemotherapeutic response and no gene signatures can add much clinical benefit for the whole patient population. We further identified a clinically homogenous subgroup of patients (ER-negative, PR-negative and HER2-negative) whose response to the chemotherapy can be reasonably predicted. Many of the discovered predictive markers for this subgroup of patients were successfully validated using a blinded validation set.