http://www.biodatamining.org/ The latest 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 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: Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs. Results: We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies. Conclusions: Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy. http://www.biodatamining.org/content/4/1/26 J. Nikolaj Dybowski Mona Riemenschneider Sascha Hauke Martin Pyka Jens Verheyen Daniel Hoffmann Dominik Heider BioData Mining 2011, null:26 2011-11-14T00:00:00Z doi:10.1186/1756-0381-4-26 Drug resistance prediction improves personalized therapy Machine-learning can be used to predict the resistance of HIV-1 to antiretroviral drugs like Bevirimat in order to identify which patients will benefit from these new drugs, thereby improving the provision of a personalized therapy. /content/figures/1756-0381-4-26-toc.gif BioData Mining 1756-0381 ${item.volume} 26 2011-11-14T00:00:00Z XML Over the last ten years, genome-wide association studies (GWAS) have reported over 4000 single nucleotide polymorphisms associated to more than 200 traits. Despite providing us with a slightly better understanding of the genetic architecture of common diseases, generating avalanches of new hypotheses, and fostering timid progress in pharmacogenomics, genetic associations studies haven't yet revolutionized clinical practice. Hence, although such studies are still published at a remarkable pace, the notion of 'post-GWAS' functional characterization of risk loci is gradually gaining in popularity. Indeed, deciphering the function of disease-associated genetic variants is likely to get us closer to achieving an understanding of disease architecture that will ultimately be translatable into clinical applications. Despite this gradual change in research priorities, the field of medical genomics remains fairly conservative: the 'single gene single disease' paradigm largely prevails, to the detriment of the avant-garde notion of 'diseasome' and of human disease network (HDN) in particular, and attempts to truly integrate clinical information (e.g., age at onset or reduction in life span) and molecular data are scarce. Here we call for a revival of the notion of disease network, and recall how superimposing layers of clinical data and biological information to such networks may help identify novel disease genes. http://www.biodatamining.org/content/4/1/25 Davnah Urbach Jason Moore BioData Mining 2011, null:25 2011-09-09T00:00:00Z doi:10.1186/1756-0381-4-25 /content/figures/1756-0381-4-25-toc.gif BioData Mining 1756-0381 ${item.volume} 25 2011-09-09T00:00:00Z 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 Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods. http://www.biodatamining.org/content/4/1/23 Camelia Chira Dragos Horvath D. Dumitrescu BioData Mining 2011, null:23 2011-07-30T00:00:00Z doi:10.1186/1756-0381-4-23 /content/figures/1756-0381-4-23-toc.gif BioData Mining 1756-0381 ${item.volume} 23 2011-07-30T00:00:00Z XML In order to characterise new bacteriocins produced by Streptococcus mutans we perform a complete bioinformatic analyses by scanning the genome sequence of strains UA159 and NN2025. By searching in the adjacent genomic context of the two-component signal transduction system we predicted the existence of many putative new bacteriocins' maturation pathways and some of them were only exclusive to a group of Streptococcus. Computational genomic and proteomic analysis combined to predictive functionnal analysis represent an alternative way for rapid identification of new putative bacteriocins as well as new potential antimicrobial drugs compared to the more traditional methods of drugs discovery using antagonism tests. http://www.biodatamining.org/content/4/1/22 Guillaume Nicolas BioData Mining 2011, null:22 2011-07-14T00:00:00Z doi:10.1186/1756-0381-4-22 /content/figures/1756-0381-4-22-toc.gif BioData Mining 1756-0381 ${item.volume} 22 2011-07-14T00:00:00Z XML Background: A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. Results: Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects. Conclusions: This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/. http://www.biodatamining.org/content/4/1/21 Daniel Himmelstein Casey Greene Jason Moore BioData Mining 2011, null:21 2011-07-07T00:00:00Z doi:10.1186/1756-0381-4-21 /content/figures/1756-0381-4-21-toc.gif BioData Mining 1756-0381 ${item.volume} 21 2011-07-07T00:00:00Z XML Background: In in a typical "left-to-right" phylogenetic tree, the vertical order of taxa is meaningless, as only the branch path between them reflects their degree of similarity. To make unresolved trees more informative, here we propose an innovative Evolutionary Algorithm (EA) method to search the best graphical representation of unresolved trees, in order to give a biological meaning to the vertical order of taxa. Methods: Starting from a West Nile virus phylogenetic tree, in a (1 + 1)-EA we evolved it by randomly rotating the internal nodes and selecting the tree with better fitness every generation. The fitness is a sum of genetic distances between the considered taxon and the r (radius) next taxa. After having set the radius to the best performance, we evolved the trees with (λ + μ)-EAs to study the influence of population on the algorithm. Results: The (1 + 1)-EA consistently outperformed a random search, and better results were obtained setting the radius to 8. The (λ + μ)-EAs performed as well as the (1 + 1), except the larger population (1000 + 1000). Conclusions: The trees after the evolution showed an improvement both of the fitness (based on a genetic distance matrix, then close taxa are actually genetically close), and of the biological interpretation. Samples collected in the same state or year moved close each other, making the tree easier to interpret. Biological relationships between samples are also easier to observe. http://www.biodatamining.org/content/4/1/20 Francesco Cerutti Luigi Bertolotti Tony Goldberg Mario Giacobini BioData Mining 2011, null:20 2011-07-01T00:00:00Z doi:10.1186/1756-0381-4-20 /content/figures/1756-0381-4-20-toc.gif BioData Mining 1756-0381 ${item.volume} 20 2011-07-01T00:00:00Z XML Background: High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths. Results: We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes. Conclusions: These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/. http://www.biodatamining.org/content/4/1/19 Sinan Erten Gurkan Bebek Rob Ewing Mehmet Koyuturk BioData Mining 2011, null:19 2011-06-24T00:00:00Z doi:10.1186/1756-0381-4-19 /content/figures/1756-0381-4-19-toc.gif BioData Mining 1756-0381 ${item.volume} 19 2011-06-24T00:00:00Z XML