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Results and data

This section incorporates novel results, useful tools, and methods using bioinformatics in new ways, including but not limited to: biological conclusions that are only supported by bioinformatics analyses.

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  1. The pathophysiological overlapping between Sjorgen’s Syndrome (SS) and HCV, presence of anti- muscarinic receptor type 3 (M3R) antibodies in SS, the role that M3R plays in the regulation of the heart rate, has...

    Authors: Sanja Glišić, David P. Cavanaugh, Krishnan K. Chittur, Milan Sencanski, Vladimir Perovic and Tijana Bojić

    Citation: BMC Bioinformatics 2016 17:139

    Content type: Research article

    Published on:

  2. Molecular structures can be represented as strings of special characters using SMILES. Since each molecule is represented as a string, the similarity between compounds can be computed using SMILES-based string...

    Authors: Hakime Öztürk, Elif Ozkirimli and Arzucan Özgür

    Citation: BMC Bioinformatics 2016 17:128

    Content type: Research Article

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  3. Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end...

    Authors: Luis de la Garza, Johannes Veit, Andras Szolek, Marc Röttig, Stephan Aiche, Sandra Gesing, Knut Reinert and Oliver Kohlbacher

    Citation: BMC Bioinformatics 2016 17:127

    Content type: Software

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  4. Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biom...

    Authors: Kévin Rue-Albrecht, Paul A. McGettigan, Belinda Hernández, Nicolas C. Nalpas, David A. Magee, Andrew C. Parnell, Stephen V. Gordon and David E. MacHugh

    Citation: BMC Bioinformatics 2016 17:126

    Content type: Software

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  5. Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of sub...

    Authors: Yafang Li, Jinyoung Byun, Guoshuai Cai, Xiangjun Xiao, Younghun Han, Olivier Cornelis, James E. Dinulos, Joe Dennis, Douglas Easton, Ivan Gorlov, Michael F. Seldin and Christopher I. Amos

    Citation: BMC Bioinformatics 2016 17:122

    Content type: Software

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  6. Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experi...

    Authors: Youzhong Liu, Kirill Smirnov, Marianna Lucio, Régis D. Gougeon, Hervé Alexandre and Philippe Schmitt-Kopplin

    Citation: BMC Bioinformatics 2016 17:114

    Content type: Methodology article

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  7. MicroRNAs (miRNAs) regulate gene expression by targeting complementary mRNAs for destruction or translational repression. Aberrant expression of miRNAs has been associated with various diseases including cance...

    Authors: Biao Liu, Jessica L. Childs-Disney, Brent M. Znosko, Dan Wang, Mohammad Fallahi, Steven M. Gallo and Matthew D. Disney

    Citation: BMC Bioinformatics 2016 17:112

    Content type: Research article

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  8. Computational modeling and simulation play an important role in analyzing the behavior of complex biological systems in response to the implantation of biomedical devices. Quantitative computational modeling d...

    Authors: Mingon Kang, Liping Tang and Jean Gao

    Citation: BMC Bioinformatics 2016 17:111

    Content type: Research Article

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  9. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments revolutionized genome-wide profiling of transcription factors and histone modifications. Although maturing sequencing technologies al...

    Authors: Qi Zhang, Xin Zeng, Sam Younkin, Trupti Kawli, Michael P. Snyder and Sündüz Keleş

    Citation: BMC Bioinformatics 2016 17:96

    Content type: Research Article

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  10. Metagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise rea...

    Authors: Minji Kim, Xiejia Zhang, Jonathan G. Ligo, Farzad Farnoud, Venugopal V. Veeravalli and Olgica Milenkovic

    Citation: BMC Bioinformatics 2016 17:94

    Content type: Methodology Article

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  11. Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuva...

    Authors: Jared S. Fowles, Kristen C. Brown, Ann M. Hess, Dawn L. Duval and Daniel L. Gustafson

    Citation: BMC Bioinformatics 2016 17:93

    Content type: Research article

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  12. Phylogenetic analysis is now an important tool in the study of viral outbreaks. It can reconstruct epidemic history when surveillance epidemiology data are sparse, and can indicate transmission linkages among ...

    Authors: Simon Dellicour, Rebecca Rose and Oliver G. Pybus

    Citation: BMC Bioinformatics 2016 17:82

    Content type: Methodolgy article

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  13. Modelling disease outbreaks often involves integrating the wealth of data that are gathered during modern outbreaks into complex mathematical or computational models of transmission. Incorporating these data i...

    Authors: Anthony O’Hare, Samantha J. Lycett, Thomas Doherty, Liliana C. M. Salvador and Rowland R. Kao

    Citation: BMC Bioinformatics 2016 17:65

    Content type: Software

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  14. The major mechanism driving cellular differentiation and organism development is the regulation of gene expression. Cis-acting enhancers and silencers have key roles in controlling gene transcription. The genomic...

    Authors: Giovanni Mele

    Citation: BMC Bioinformatics 2016 17:50

    Content type: Software

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  15. Computational biologists daily face the need to explore massive amounts of genomic data. New visualization techniques can help researchers navigate and understand these big data. Horizon Charts are a relativel...

    Authors: David Rio Deiros, Richard A. Gibbs and Jeffrey Rogers

    Citation: BMC Bioinformatics 2016 17:49

    Content type: Software

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  16. Selecting a parsimonious set of informative genes to build highly generalized performance classifier is the most important task for the analysis of tumor microarray expression data. Many existing gene pair eva...

    Authors: Yuan Chen, Lifeng Wang, Lanzhi Li, Hongyan Zhang and Zheming Yuan

    Citation: BMC Bioinformatics 2016 17:44

    Content type: Research article

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  17. Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can ...

    Authors: Kerstin Johnsson, Jonas Wallin and Magnus Fontes

    Citation: BMC Bioinformatics 2016 17:25

    Content type: Research Article

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    The Erratum to this article has been published in BMC Bioinformatics 2016 17:149

  18. Hepatocellular carcinoma (HCC) is an aggressive epithelial tumor which shows very poor prognosis and high rate of recurrence, representing an urgent problem for public healthcare. MicroRNAs (miRNAs/miRs) are a...

    Authors: Filippo Del Vecchio, Francesco Gallo, Antinisca Di Marco, Valentina Mastroiaco, Pasquale Caianiello, Francesca Zazzeroni, Edoardo Alesse and Alessandra Tessitore

    Citation: BMC Bioinformatics 2015 16:408

    Content type: Research article

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  19. Identification of one or several disease causing variant(s) from the large collection of variants present in an individual is often achieved by the sequential use of heuristic filters. The recent development o...

    Authors: Bart J. G. Broeckx, Frank Coopman, Geert Verhoeven, Tim Bosmans, Ingrid Gielen, Walter Dingemanse, Jimmy H. Saunders, Dieter Deforce and Filip Van Nieuwerburgh

    Citation: BMC Bioinformatics 2015 16:391

    Content type: Software

    Published on:

  20. Plasma miRNAs have the potential as cancer biomarkers but no consolidated guidelines for data mining in this field are available. The purpose of the study was to apply a supervised data analysis strategy in a ...

    Authors: Elena Landoni, Rosalba Miceli, Maurizio Callari, Paola Tiberio, Valentina Appierto, Valentina Angeloni, Luigi Mariani and Maria Grazia Daidone

    Citation: BMC Bioinformatics 2015 16:388

    Content type: Research article

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  21. Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model ...

    Authors: Victor Mocioiu, Sandra Ortega-Martorell, Iván Olier, Michal Jablonski, Jana Starcukova, Paulo Lisboa, Carles Arús and Margarida Julià-Sapé

    Citation: BMC Bioinformatics 2015 16:378

    Content type: Software

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  22. Bacterial operons are considerably more complex than what were thought. At least their components are dynamically rather than statically defined as previously assumed. Here we present a computational study of ...

    Authors: Xizeng Mao, Qin Ma, Bingqiang Liu, Xin Chen, Hanyuan Zhang and Ying Xu

    Citation: BMC Bioinformatics 2015 16:356

    Content type: Research article

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  23. ChIP-seq experiments are widely used to detect and study DNA-protein interactions, such as transcription factor binding and chromatin modifications. However, downstream analysis of ChIP-seq data is currently r...

    Authors: Marzia A. Cremona, Laura M. Sangalli, Simone Vantini, Gaetano I. Dellino, Pier Giuseppe Pelicci, Piercesare Secchi and Laura Riva

    Citation: BMC Bioinformatics 2015 16:349

    Content type: Research article

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  24. Age-related macular degeneration (AMD) is the leading cause of irreversible visual loss in the elderly in developed countries and typically affects more than 10 % of individuals over age 80. AMD has a large ge...

    Authors: Jacob B. Hall, Jessica N. Cooke Bailey, Joshua D. Hoffman, Margaret A. Pericak-Vance, William K. Scott, Jaclyn L. Kovach, Stephen G. Schwartz, Anita Agarwal, Milam A. Brantley Jr., Jonathan L. Haines and William S. Bush

    Citation: BMC Bioinformatics 2015 16:329

    Content type: Research article

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  25. We are creating software for agent-based simulation and visualization of bio-molecular processes in bacterial and eukaryotic cells. As a first example, we have built a 3-dimensional, interactive computer model...

    Authors: Afshin Esmaeili, Timothy Davison, Andrew Wu, Joenel Alcantara and Christian Jacob

    Citation: BMC Bioinformatics 2015 16:311

    Content type: Research Article

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  26. One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make ...

    Authors: Askar Obulkasim, Maarten Fornerod, Michel C. Zwaan, Dirk Reinhardt and Marry M. van den Heuvel-Eibrink

    Citation: BMC Bioinformatics 2015 16:305

    Content type: Research Article

    Published on:

  27. Codon usage plays a crucial role when recombinant proteins are expressed in different organisms. This is especially the case if the codon usage frequency of the organism of origin and the target host organism ...

    Authors: Edward Daniel, Goodluck U. Onwukwe, Rik K. Wierenga, Susan E. Quaggin, Seppo J. Vainio and Mirja Krause

    Citation: BMC Bioinformatics 2015 16:303

    Content type: Software

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  28. Studies regarding coxsackievirus (CV) tend to focus on epidemic outbreaks, an imbalanced topology is considered to be an indication of acute infection with partial cross-immunity. In enteroviruses, a clear und...

    Authors: Hui-Wen Huang, Yao-Shen Chen, Jeff Yi-Fu Chen, Po-Liang Lu, Yung-Cheng Lin, Bao-Chen Chen, Li-Chiu Chou, Chu-Feng Wang, Hui-Ju Su, Yi-Chien Huang, Yong-Ying Shi, Hsiu-Lin Chen, Bintou Sanno-Duanda, Tsi-Shu Huang, Kuei-Hsiang Lin, Yu-Chang Tyan…

    Citation: BMC Bioinformatics 2015 16:302

    Content type: Research article

    Published on:

  29. In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly...

    Authors: Rok Blagus and Lara Lusa

    Citation: BMC Bioinformatics 2015 16:300

    Content type: Research Article

    Published on:

  30. In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present...

    Authors: Abdulkadir Elmas, Xiaodong Wang and Michael S. Samoilov

    Citation: BMC Bioinformatics 2015 16:299

    Content type: Methodology Article

    Published on:

  31. T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, pr...

    Authors: Andres H. Gutiérrez, William D. Martin, Chris Bailey-Kellogg, Frances Terry, Leonard Moise and Anne S. De Groot

    Citation: BMC Bioinformatics 2015 16:290

    Content type: Research article

    Published on:

  32. During the last decade, many scientific works have concerned the possible use of miRNA levels as diagnostic and prognostic tools for different kinds of cancer. The development of reliable classifiers requires ...

    Authors: Leonardo Ricci, Valerio Del Vescovo, Chiara Cantaloni, Margherita Grasso, Mattia Barbareschi and Michela Alessandra Denti

    Citation: BMC Bioinformatics 2015 16:287

    Content type: Methodology Article

    Published on:

  33. The study of nuclear architecture using Chromosome Conformation Capture (3C) technologies is a novel frontier in biology. With further reduction in sequencing costs, the potential of Hi-C in describing nuclear...

    Authors: Marc W. Schmid, Stefan Grob and Ueli Grossniklaus

    Citation: BMC Bioinformatics 2015 16:277

    Content type: Software

    Published on:

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2019 Journal Metrics

  • Citation Impact
    3.242 - 2-year Impact Factor
    3.213 - 5-year Impact Factor
    1.156 - Source Normalized Impact per Paper (SNIP)
    1.626 - SCImago Journal Rank (SJR)

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