Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

The joint NETTAB/Integrative Bioinformatics 2015 Meeting: aims, topics and outcomes

Romano P, Hofestädt R, Lange M, D'Elia D. The joint NETTAB/Integrative Bioinformatics 2015 Meeting: aims, topics and outcomes. BMC BIOINFORMATICS. 2017;18(S5): 101.The 15th International NETTAB workshop and the 11th Integrative Bioinformatics Symposium were held together in Bari, on October 14-16, 2016, as Joint NETTAB/IB 2015 Meeting. A special topic for the meeting was "Bioinformatics for ncRNA", but the traditional topics of both meetings series were also included in the event. About 60 scientific contributions were presented, including six keynote lectures, one special guest lecture, and many oral communications and posters. A " Two-Day Hands-on Tutorial" event was organised before the workshop. Selected full papers from some of the best works presented in Bari were submitted either to the Journal of Integrative Bioinformatics or to a purpose Call for a Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been either accepted for publication in this Supplement or published in the Journal of Integrative Bioinformatics, for a more complete presentation of the outcomes of the meeting

Integrative analysis of large-scale biological data sets

We present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular combinations of different analysis types, to extract new biological insights from experimental transcriptomics and proteomics data. They enable researchers to combine related algorithms and datasets to increase the robustness and accuracy of statistical analyses and exploit synergies of different computational methods, ranging from statistical learning to optimization and topological network analysis

Exploring microRNA Biology using Integrative Bioinformatics

Deregulation of energy metabolism is one of the emerging hallmarks of cancer required for proliferation and metastasis. MicroRNAs are small RNA molecules that have crucial roles in the regulation of biological processes in organisms, including metabolism. Due to recent discovery of miRNAs in humans, roles of miRNAs in metabolism of tumour cells, and effects these have on cancer patients, are still obscure and in need of expansion. Currently, experimental and computational data on the miRNAs are being analysed by a wide range of statistical methods; however, these methods in their original forms posses many limitations. Therefore, new ways of utilising these statistical methods are needed in order to unravel the roles of miRNAs in cancer metabolism. In this thesis, the roles of a specific miRNA, miR-22, and the three metabolic target genes were investigated through the use of classical statistical methods, revealed that miR-22, the metabolic target genes, and the interactions between them, were beneficial to survival outcome of breast cancer patients. Furthermore, novel combinations of the conventional statistical methods were invented in order to investigate the global miRNA regulations on metabolic target genes. These new procedures were demonstrated by using publicly available data sets. In one analysis, it was found that miRNAs could be divided into six clusters according to the metabolic target genes through a novel combination of statistical methods. A new statistical method was also invented to provide a generalised means to test for clustering based on sets of correlations.Open Acces

Outcome of the First wwPDB Hybrid / Integrative Methods Task Force Workshop

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models

Specifications of standards in systems and synthetic biology: Status and developments in 2020

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology

BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism

Prediction of protein-protein interactions using one-class classification methods and integrating diverse data

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