bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract 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

Summary 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

SummaryA 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

SummaryThe 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

AbstractIncreasingly, 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

SummaryNumerous 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

AbstractIn 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

SummaryMining 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

Summary 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

SummaryWith 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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.