Skip to content


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.

Page 2 of 8
  1. 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

    Published on:

  2. 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

    Published on:

  3. 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

    Published on:

  4. 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

    Published on:

  5. 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

    Published on:

  6. 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

    Published on:

  7. 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

    Published on:

  8. 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

    Published on:

  9. 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

    Published on:

  10. Content type: Software

    The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were de...

    Authors: Qing Wei, Ishita K. Khan, Ziyun Ding, Satwica Yerneni and Daisuke Kihara

    Citation: BMC Bioinformatics 2017 18:177

    Published on:

  11. Content type: Research Article

    Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based...

    Authors: Pathima Nusrath Hameed, Karin Verspoor, Snezana Kusljic and Saman Halgamuge

    Citation: BMC Bioinformatics 2017 18:140

    Published on:

  12. Content type: Research article

    The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, w...

    Authors: Min Oh, Jaegyoon Ahn, Taekeon Lee, Giup Jang, Chihyun Park and Youngmi Yoon

    Citation: BMC Bioinformatics 2017 18:131

    Published on:

  13. Content type: Research article

    The large-scale analysis of phenomic data (i.e., full phenotypic traits of an organism, such as shape, metabolic substrates, and growth conditions) in microbial bioinformatics has been hampered by the lack of ...

    Authors: Jin Mao, Lisa R. Moore, Carrine E. Blank, Elvis Hsin-Hui Wu, Marcia Ackerman, Sonali Ranade and Hong Cui

    Citation: BMC Bioinformatics 2016 17:528

    Published on:

Page 2 of 8

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