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BMC Bioinformatics

Knowledge-based analysis

Section edited by Hagit Shatkay

This section incorporates all aspects of knowledge-based analysis in biology including but not limited to: methods for the processing of text, ontologies and other computational representations of biological knowledge, as well as applications of knowledge-based systems for gaining insight into biology and biological data.

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  1. Content type: Research Article

    Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health inf...

    Authors: Halil Kilicoglu, Asma Ben Abacha, Yassine Mrabet, Sonya E. Shooshan, Laritza Rodriguez, Kate Masterton and Dina Demner-Fushman

    Citation: BMC Bioinformatics 2018 19:34

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  2. Content type: Research Article

    Application Programming Interfaces (APIs) are now widely used to distribute biological data. And many popular biological APIs developed by many different research teams have adopted Javascript Object Notation ...

    Authors: Jiwen Xin, Cyrus Afrasiabi, Sebastien Lelong, Julee Adesara, Ginger Tsueng, Andrew I. Su and Chunlei Wu

    Citation: BMC Bioinformatics 2018 19:30

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  3. Content type: Research Article

    Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and ...

    Authors: Kyubum Lee, Byounggun Kim, Yonghwa Choi, Sunkyu Kim, Wonho Shin, Sunwon Lee, Sungjoon Park, Seongsoon Kim, Aik Choon Tan and Jaewoo Kang

    Citation: BMC Bioinformatics 2018 19:21

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  4. Content type: Research Article

    The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid ...

    Authors: Juan Miguel Cejuela, Shrikant Vinchurkar, Tatyana Goldberg, Madhukar Sollepura Prabhu Shankar, Ashish Baghudana, Aleksandar Bojchevski, Carsten Uhlig, André Ofner, Pandu Raharja-Liu, Lars Juhl Jensen and Burkhard Rost

    Citation: BMC Bioinformatics 2018 19:15

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  5. Content type: Methodology article

    Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional re...

    Authors: Miguel Ángel Rodríguez-García and Robert Hoehndorf

    Citation: BMC Bioinformatics 2018 19:7

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  6. Content type: Methodology Article

    Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary ‘co-data’ can be used to improve the performance of a Random ...

    Authors: Dennis E. te Beest, Steven W. Mes, Saskia M. Wilting, Ruud H. Brakenhoff and Mark A. van de Wiel

    Citation: BMC Bioinformatics 2017 18:584

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  7. Content type: Methodology Article

    In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practic...

    Authors: Bart J. G. Broeckx, Luc Peelman, Jimmy H. Saunders, Dieter Deforce and Lieven Clement

    Citation: BMC Bioinformatics 2017 18:535

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  8. Content type: Methodology Article

    Researchers have previously developed a multitude of methods designed to identify biological pathways associated with specific clinical or experimental conditions of interest, with the aim of facilitating biol...

    Authors: Chenggang Yu, Hyung Jun Woo, Xueping Yu, Tatsuya Oyama, Anders Wallqvist and Jaques Reifman

    Citation: BMC Bioinformatics 2017 18:453

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  9. Content type: Research Article

    The prediction of human gene–abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with ...

    Authors: Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini

    Citation: BMC Bioinformatics 2017 18:449

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  10. Content type: Research Article

    Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based s...

    Authors: Wei Zheng, Hongfei Lin, Ling Luo, Zhehuan Zhao, Zhengguang Li, Yijia Zhang, Zhihao Yang and Jian Wang

    Citation: BMC Bioinformatics 2017 18:445

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  11. Content type: Research Article

    Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling techno...

    Authors: Maria Taboada, Hadriana Rodriguez, Ranga C. Gudivada and Diego Martinez

    Citation: BMC Bioinformatics 2017 18:446

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  12. Content type: Research Article

    The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-...

    Authors: A. Eck, L. M. Zintgraf, E. F. J. de Groot, T. G. J. de Meij, T. S. Cohen, P. H. M. Savelkoul, M. Welling and A. E. Budding

    Citation: BMC Bioinformatics 2017 18:441

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  13. Content type: Research Article

    Coreference resolution is the task of finding strings in text that have the same referent as other strings. Failures of coreference resolution are a common cause of false negatives in information extraction fr...

    Authors: K. Bretonnel Cohen, Arrick Lanfranchi, Miji Joo-young Choi, Michael Bada, William A. Baumgartner Jr., Natalya Panteleyeva, Karin Verspoor, Martha Palmer and Lawrence E. Hunter

    Citation: BMC Bioinformatics 2017 18:372

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2016 Journal Metrics

  • Citation Impact
    2.448 - 2-year Impact Factor
    3.450 - 5-year Impact Factor
    0.946 - Source Normalized Impact per Paper (SNIP)
    1.467 - SCImago Journal Rank (SJR)

    1405 Usage Factor

    Social Media Impact
    816 mentions