http://www.biodatamining.org/ The most accessed research articles published by BioData Mining 2012-03-26T00:00:00Z Background: Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods: We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results: The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions: By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications. http://www.biodatamining.org/content/5/1/2 Johannes Tuikkala Heidi Vahamaa Pekka Salmela Olli Nevalainen Tero Aittokallio BioData Mining 2012, null:2 2012-03-26T00:00:00Z doi:10.1186/1756-0381-5-2 Generating network graphs for large datasets New multilevel network layout algorithms allow researchers to create biologically relevant and visually pleasing network graphs for large datasets within a multi optimization framework. /content/figures/1756-0381-5-2-toc.gif BioData Mining 1756-0381 ${item.volume} 2 2012-03-26T00:00:00Z XML Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. http://www.biodatamining.org/content/4/1/10 Georgios Pavlopoulos Maria Secrier Charalampos Moschopoulos Theodoros Soldatos Sophia Kossida Jan Aerts Reinhard Schneider Pantelis Bagos BioData Mining 2011, null:10 2011-04-28T00:00:00Z doi:10.1186/1756-0381-4-10 /content/figures/1756-0381-4-10-toc.gif BioData Mining 1756-0381 ${item.volume} 10 2011-04-28T00:00:00Z XML The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis. http://www.biodatamining.org/content/3/1/1 Georgios Pavlopoulos Theodoros Soldatos Adriano Barbosa-Silva Reinhard Schneider BioData Mining 2010, null:1 2010-02-22T00:00:00Z doi:10.1186/1756-0381-3-1 /content/figures/1756-0381-3-1-toc.gif BioData Mining 1756-0381 ${item.volume} 1 2010-02-22T00:00:00Z XML The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing. http://www.biodatamining.org/content/1/1/12 Georgios Pavlopoulos Anna-Lynn Wegener Reinhard Schneider BioData Mining 2008, null:12 2008-11-28T00:00:00Z doi:10.1186/1756-0381-1-12 Biological network visualization /content/figures/1756-0381-1-12-toc.gif BioData Mining 1756-0381 ${item.volume} 12 2008-11-28T00:00:00Z XML Background: Keeping up-to-date with bioscience literature is becoming increasingly challenging. Several recent methods help meet this challenge by allowing literature search to be launched based on lists of abstracts that the user judges to be 'interesting'. Some methods go further by allowing the user to provide a second input set of 'uninteresting' abstracts; these two input sets are then used to search and rank literature by relevance. In this work we present the service 'Caipirini' (http://caipirini.org) that also allows two input sets, but takes the novel approach of allowing ranking of literature based on one or more sets of genes. Results: To evaluate the usefulness of Caipirini, we used two test cases, one related to the human cell cycle, and a second related to disease defense mechanisms in Arabidopsis thaliana. In both cases, the new method achieved high precision in finding literature related to the biological mechanisms underlying the input data sets. Conclusions: To our knowledge Caipirini is the first service enabling literature search directly based on biological relevance to gene sets; thus, Caipirini gives the research community a new way to unlock hidden knowledge from gene sets derived via high-throughput experiments. http://www.biodatamining.org/content/5/1/1 Theodoros Soldatos Sean O'Donoghue Venkata Satagopam Adriano Barbosa-Silva Georgios Pavlopoulos Ana Carolina Wanderley-Nogueira Nina Mota Soares-Cavalcanti Reinhard Schneider BioData Mining 2012, null:1 2012-02-01T00:00:00Z doi:10.1186/1756-0381-5-1 New software uses gene sets to rank literature 'Caipirini', a new software program, allows ranking of biomedical literature based on biological relevance to gene sets, thereby enabling data from these high-throughput experiments to be more easily accessed. /content/figures/1756-0381-5-1-toc.gif BioData Mining 1756-0381 ${item.volume} 1 2012-02-01T00:00:00Z XML Background: In computer science, database systems are based on the relational model founded by Edgar Codd in 1970. On the other hand, in the area of biology the word 'database' often refers to loosely formatted, very large text files. Although such bio-databases may describe conflicts or ambiguities (e.g. a protein pair do and do not interact, or unknown parameters) in a positive sense, the flexibility of the data format sacrifices a systematic query mechanism equivalent to the widely used SQL. Results: To overcome this disadvantage, we propose embeddable string-search commands on a Wiki-based system and designed a half-formatted database. As proof of principle, a database of flavonoid with 6902 molecular structures from over 1687 plant species was implemented on MediaWiki, the background system of Wikipedia. Registered users can describe any information in an arbitrary format. Structured part is subject to text-string searches to realize relational operations. The system was written in PHP language as the extension of MediaWiki. All modifications are open-source and publicly available. Conclusion: This scheme benefits from both the free-formatted Wiki style and the concise and structured relational-database style. MediaWiki supports multi-user environments for document management, and the cost for database maintenance is alleviated. http://www.biodatamining.org/content/1/1/7 Masanori Arita Kazuhiro Suwa BioData Mining 2008, null:7 2008-09-17T00:00:00Z doi:10.1186/1756-0381-1-7 /content/figures/1756-0381-1-7-toc.gif BioData Mining 1756-0381 ${item.volume} 7 2008-09-17T00:00:00Z XML Background: Genome-sequencing projects are currently producing an enormous amount of new sequences and cause the rapid increasing of protein sequence databases. The unsupervised classification of these data into functional groups or families, clustering, has become one of the principal research objectives in structural and functional genomics. Computer programs to automatically and accurately classify sequences into families become a necessity. A significant number of methods have addressed the clustering of protein sequences and most of them can be categorized in three major groups: hierarchical, graph-based and partitioning methods. Among the various sequence clustering methods in literature, hierarchical and graph-based approaches have been widely used. Although partitioning clustering techniques are extremely used in other fields, few applications have been found in the field of protein sequence clustering. It is not fully demonstrated if partitioning methods can be applied to protein sequence data and if these methods can be efficient compared to the published clustering methods. Methods: We developed four partitioning clustering approaches using Smith-Waterman local-alignment algorithm to determine pair-wise similarities of sequences. Four different sets of protein sequences were used as evaluation data sets for the proposed methods. Results: We show that these methods outperform several other published clustering methods in terms of correctly predicting a classifier and especially in terms of the correctness of the provided prediction. The software is available to academic users from the authors upon request. http://www.biodatamining.org/content/2/1/3 Sondes Fayech Nadia Essoussi Mohamed Limam BioData Mining 2009, null:3 2009-04-02T00:00:00Z doi:10.1186/1756-0381-2-3 /content/figures/1756-0381-2-3-toc.gif BioData Mining 1756-0381 ${item.volume} 3 2009-04-02T00:00:00Z XML Background: MicroRNAs (miRNAs) mediate posttranscriptional regulation of protein-coding genes by binding to the 3' untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation, depending on the degree of sequence complementarity. In general, a single miRNA concurrently downregulates hundreds of target mRNAs. Thus, miRNAs play a key role in fine-tuning of diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. However, it remains to be fully elucidated whether a set of miRNA target genes regulated by an individual miRNA in the whole human microRNAome generally constitute the biological network of functionally-associated molecules or simply reflect a random set of functionally-independent genes. Methods: The complete set of human miRNAs was downloaded from miRBase Release 16. We explored target genes of individual miRNA by using the Diana-microT 3.0 target prediction program, and selected the genes with the miTG score ≧ 20 as the set of highly reliable targets. Then, Entrez Gene IDs of miRNA target genes were uploaded onto KeyMolnet, a tool for analyzing molecular interactions on the comprehensive knowledgebase by the neighboring network-search algorithm. The generated network, compared side by side with human canonical networks of the KeyMolnet library, composed of 430 pathways, 885 diseases, and 208 pathological events, enabled us to identify the canonical network with the most significant relevance to the extracted network. Results: Among 1,223 human miRNAs examined, Diana-microT 3.0 predicted reliable targets from 273 miRNAs. Among them, KeyMolnet successfully extracted molecular networks from 232 miRNAs. The most relevant pathway is transcriptional regulation by transcription factors RB/E2F, the disease is adult T cell lymphoma/leukemia, and the pathological event is cancer. Conclusion: The predicted targets derived from approximately 20% of all human miRNAs constructed biologically meaningful molecular networks, supporting the view that a set of miRNA targets regulated by a single miRNA generally constitute the biological network of functionally-associated molecules in human cells. http://www.biodatamining.org/content/4/1/17 Jun-ichi Satoh Hiroko Tabunoki BioData Mining 2011, null:17 2011-06-17T00:00:00Z doi:10.1186/1756-0381-4-17 /content/figures/1756-0381-4-17-toc.gif BioData Mining 1756-0381 ${item.volume} 17 2011-06-17T00:00:00Z XML Background: A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results: We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions: MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. http://www.biodatamining.org/content/4/1/24 Stacey Winham Alison Motsinger-Reif BioData Mining 2011, null:24 2011-08-16T00:00:00Z doi:10.1186/1756-0381-4-24 /content/figures/1756-0381-4-24-toc.gif BioData Mining 1756-0381 ${item.volume} 24 2011-08-16T00:00:00Z XML Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. http://www.biodatamining.org/content/1/1/6 Ruben Armananzas Inaki Inza Roberto Santana Yvan Saeys Jose Flores Jose Lozano Yves Van de Peer Rosa Blanco Victor Robles Concha Bielza Pedro Larranaga BioData Mining 2008, null:6 2008-09-11T00:00:00Z doi:10.1186/1756-0381-1-6 /content/figures/1756-0381-1-6-toc.gif BioData Mining 1756-0381 ${item.volume} 6 2008-09-11T00:00:00Z XML