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Guest editorial. Computational systems biology.

IET systems biology

Chen L.
PMID: 24067411
IET Syst Biol. 2013 Oct;7(5):125. doi: 10.1049/iet-syb.2013.0019.

No abstract available.

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Chen L. Guest editorial. Computational systems biology. IET Syst Biol. 2013;7(5):125doi: 10.1049/iet-syb.2013.0019.
Chen, L. (2013). Guest editorial. Computational systems biology. IET systems biology, 7(5), 125. https://doi.org/10.1049/iet-syb.2013.0019
Chen, Luonan. "Guest editorial. Computational systems biology." IET systems biology vol. 7,5 (2013): 125. doi: https://doi.org/10.1049/iet-syb.2013.0019
Chen L. Guest editorial. Computational systems biology. IET Syst Biol. 2013 Oct;7(5):125. doi: 10.1049/iet-syb.2013.0019. PMID: 24067411.
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SNPTransformer: a lightweight toolkit for genome-wide association studies.

Genomics, proteomics & bioinformatics

Dong C.
PMID: 21382596
Genomics Proteomics Bioinformatics. 2010 Dec;8(4):268-73. doi: 10.1016/S1672-0229(10)60029-0.

High-throughput genotyping chips have produced huge datasets for genome-wide association studies (GWAS) that have contributed greatly to discovering susceptibility genes for complex diseases. There are two strategies for performing data analysis for GWAS. One strategy is to use open-source...

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Dong C. SNPTransformer: a lightweight toolkit for genome-wide association studies. Genomics Proteomics Bioinformatics. 2010;8(4):268-73doi: 10.1016/S1672-0229(10)60029-0.
Dong, C. (2010). SNPTransformer: a lightweight toolkit for genome-wide association studies. Genomics, proteomics & bioinformatics, 8(4), 268-73. https://doi.org/10.1016/S1672-0229(10)60029-0
Dong, Changzheng. "SNPTransformer: a lightweight toolkit for genome-wide association studies." Genomics, proteomics & bioinformatics vol. 8,4 (2010): 268-73. doi: https://doi.org/10.1016/S1672-0229(10)60029-0
Dong C. SNPTransformer: a lightweight toolkit for genome-wide association studies. Genomics Proteomics Bioinformatics. 2010 Dec;8(4):268-73. doi: 10.1016/S1672-0229(10)60029-0. PMID: 21382596; PMCID: PMC5054149.
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[Bioinformatics and Next -generation Sequencing].

Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti

Krejčí A, Müller P, Vojtěšek B.
PMID: 26374164
Klin Onkol. 2015;28:2S91-6. doi: 10.14735/amko20152s91.

Next-generation sequencing technologies are currently well-established in the research field and progressively find their way towards clinical applications. Sequencers produce vast amounts of data and therefore bioinformatics methods are needed for processing. Without computational methods, sequencing would not be...

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Krejčí A, Müller P, Vojtěšek B. Bioinformatika a sekvenování nové generace. [Bioinformatics and Next -generation Sequencing]. Klin Onkol. 2015;28:2S91-6doi: 10.14735/amko20152s91.
Krejčí, A., Müller, P., & Vojtěšek, B. (2015). Bioinformatika a sekvenování nové generace. [Bioinformatics and Next -generation Sequencing]. Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti, 282S91-6. https://doi.org/10.14735/amko20152s91
Krejčí, A, et al. "Bioinformatika a sekvenování nové generace." [Bioinformatics and Next -generation Sequencing]. Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti vol. 28 (2015): 2S91-6. doi: https://doi.org/10.14735/amko20152s91
Krejčí A, Müller P, Vojtěšek B. Bioinformatika a sekvenování nové generace. [Bioinformatics and Next -generation Sequencing]. Klin Onkol. 2015;28:2S91-6. doi: 10.14735/amko20152s91. Czech. PMID: 26374164.
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An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS.

Methods in molecular biology (Clifton, N.J.)

Silva GGZ, Lopes FAC, Edwards RA.
PMID: 28451970
Methods Mol Biol. 2017;1611:35-44. doi: 10.1007/978-1-4939-7015-5_4.

One of the main goals in metagenomics is to identify the functional profile of a microbial community from unannotated shotgun sequencing reads. Functional annotation is important in biological research because it enables researchers to identify the abundance of functional...

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Silva GGZ, Lopes FAC, Edwards RA. An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS. Methods Mol Biol. 2017;1611:35-44doi: 10.1007/978-1-4939-7015-5_4.
Silva, G. G. Z., Lopes, F. A. C., & Edwards, R. A. (2017). An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS. Methods in molecular biology (Clifton, N.J.), 161135-44. https://doi.org/10.1007/978-1-4939-7015-5_4
Silva, Genivaldo Gueiros Z, et al. "An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS." Methods in molecular biology (Clifton, N.J.) vol. 1611 (2017): 35-44. doi: https://doi.org/10.1007/978-1-4939-7015-5_4
Silva GGZ, Lopes FAC, Edwards RA. An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS. Methods Mol Biol. 2017;1611:35-44. doi: 10.1007/978-1-4939-7015-5_4. PMID: 28451970.
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Enzyme Annotation and Metabolic Reconstruction Using KEGG.

Methods in molecular biology (Clifton, N.J.)

Kanehisa M.
PMID: 28451977
Methods Mol Biol. 2017;1611:135-145. doi: 10.1007/978-1-4939-7015-5_11.

KEGG is an integrated database resource for linking sequences to biological functions from molecular to higher levels. Knowledge on molecular functions is stored in the KO (KEGG Orthology) database, while cellular- and organism-level functions are represented in the PATHWAY...

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Kanehisa M. Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods Mol Biol. 2017;1611:135-145doi: 10.1007/978-1-4939-7015-5_11.
Kanehisa, M. (2017). Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods in molecular biology (Clifton, N.J.), 1611135-145. https://doi.org/10.1007/978-1-4939-7015-5_11
Kanehisa, Minoru. "Enzyme Annotation and Metabolic Reconstruction Using KEGG." Methods in molecular biology (Clifton, N.J.) vol. 1611 (2017): 135-145. doi: https://doi.org/10.1007/978-1-4939-7015-5_11
Kanehisa M. Enzyme Annotation and Metabolic Reconstruction Using KEGG. Methods Mol Biol. 2017;1611:135-145. doi: 10.1007/978-1-4939-7015-5_11. PMID: 28451977.
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SSER: Species specific essential reactions database.

BMC systems biology

Labena AA, Ye YN, Dong C, Zhang FZ, Guo FB.
PMID: 28420402
BMC Syst Biol. 2017 Apr 19;11(1):50. doi: 10.1186/s12918-017-0426-0.

BACKGROUND: Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction...

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Labena AA, Ye YN, Dong C, et al. SSER: Species specific essential reactions database. BMC Syst Biol. 2017;11(1):50doi: 10.1186/s12918-017-0426-0.
Labena, A. A., Ye, Y. N., Dong, C., Zhang, F. Z., & Guo, F. B. (2017). SSER: Species specific essential reactions database. BMC systems biology, 11(1), 50. https://doi.org/10.1186/s12918-017-0426-0
Labena, Abraham A, et al. "SSER: Species specific essential reactions database." BMC systems biology vol. 11,1 (2017): 50. doi: https://doi.org/10.1186/s12918-017-0426-0
Labena AA, Ye YN, Dong C, Zhang FZ, Guo FB. SSER: Species specific essential reactions database. BMC Syst Biol. 2017 Apr 19;11(1):50. doi: 10.1186/s12918-017-0426-0. PMID: 28420402; PMCID: PMC5395902.
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DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia.

Bioinformatics (Oxford, England)

Heirendt L, Thiele I, Fleming RMT.
PMID: 28453682
Bioinformatics. 2017 May 01;33(9):1421-1423. doi: 10.1093/bioinformatics/btw838.

MOTIVATION: Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.RESULTS: DistributedFBA.jl is a...

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Heirendt L, Thiele I, Fleming RMT. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics. 2017;33(9):1421-1423doi: 10.1093/bioinformatics/btw838.
Heirendt, L., Thiele, I., & Fleming, R. M. T. (2017). DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics (Oxford, England), 33(9), 1421-1423. https://doi.org/10.1093/bioinformatics/btw838
Heirendt, Laurent, et al. "DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia." Bioinformatics (Oxford, England) vol. 33,9 (2017): 1421-1423. doi: https://doi.org/10.1093/bioinformatics/btw838
Heirendt L, Thiele I, Fleming RMT. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics. 2017 May 01;33(9):1421-1423. doi: 10.1093/bioinformatics/btw838. PMID: 28453682; PMCID: PMC5408791.
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A heuristic approach to determine an appropriate number of topics in topic modeling.

BMC bioinformatics

Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W.
PMID: 26424364
BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25.

BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics...

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Zhao W, Chen JJ, Perkins R, et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8doi: 10.1186/1471-2105-16-S13-S8.
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC bioinformatics, 16S8. https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao, Weizhong, et al. "A heuristic approach to determine an appropriate number of topics in topic modeling." BMC bioinformatics vol. 16 (2015): S8. doi: https://doi.org/10.1186/1471-2105-16-S13-S8
Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, Zou W. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16:S8. doi: 10.1186/1471-2105-16-S13-S8. Epub 2015 Sep 25. PMID: 26424364; PMCID: PMC4597325.
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tmBioC: improving interoperability of text-mining tools with BioC.

Database : the journal of biological databases and curation

Khare R, Wei CH, Mao Y, Leaman R, Lu Z.
PMID: 25062914
Database (Oxford). 2014 Jul 25;2014. doi: 10.1093/database/bau073. Print 2014.

The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to...

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Khare R, Wei CH, Mao Y, et al. tmBioC: improving interoperability of text-mining tools with BioC. Database (Oxford). 2014;2014doi: 10.1093/database/bau073.
Khare, R., Wei, C. H., Mao, Y., Leaman, R., & Lu, Z. (2014). tmBioC: improving interoperability of text-mining tools with BioC. Database : the journal of biological databases and curation, 2014. https://doi.org/10.1093/database/bau073
Khare, Ritu, et al. "tmBioC: improving interoperability of text-mining tools with BioC." Database : the journal of biological databases and curation vol. 2014 (2014). doi: https://doi.org/10.1093/database/bau073
Khare R, Wei CH, Mao Y, Leaman R, Lu Z. tmBioC: improving interoperability of text-mining tools with BioC. Database (Oxford). 2014 Jul 25;2014. doi: 10.1093/database/bau073. Print 2014. PMID: 25062914; PMCID: PMC4110697.
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Current Progress in Bioinformatics 2016.

Briefings in bioinformatics

Altman R.
PMID: 26628559
Brief Bioinform. 2016 Jan;17(1):1. doi: 10.1093/bib/bbv105. Epub 2015 Nov 30.

No abstract available.

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Altman R. Current Progress in Bioinformatics 2016. Brief Bioinform. 2015;17(1):1doi: 10.1093/bib/bbv105.
Altman, R. (2016). Current Progress in Bioinformatics 2016. Briefings in bioinformatics, 17(1), 1. https://doi.org/10.1093/bib/bbv105
Altman, Russ. "Current Progress in Bioinformatics 2016." Briefings in bioinformatics vol. 17,1 (2016): 1. doi: https://doi.org/10.1093/bib/bbv105
Altman R. Current Progress in Bioinformatics 2016. Brief Bioinform. 2016 Jan;17(1):1. doi: 10.1093/bib/bbv105. Epub 2015 Nov 30. PMID: 26628559.
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mORCA: sailing bioinformatics world with mobile devices.

Bioinformatics (Oxford, England)

Díaz-Del-Pino S, Falgueras J, Perez-Wohlfeil E, Trelles O.
PMID: 29069310
Bioinformatics. 2018 Mar 01;34(5):869-870. doi: 10.1093/bioinformatics/btx673.

MOTIVATION: Nearly 10 years have passed since the first mobile apps appeared. Given the fact that bioinformatics is a web-based world and that mobile devices are endowed with web-browsers, it seemed natural that bioinformatics would transit from personal computers...

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Díaz-Del-Pino S, Falgueras J, Perez-Wohlfeil E, et al. mORCA: sailing bioinformatics world with mobile devices. Bioinformatics. 2018;34(5):869-870doi: 10.1093/bioinformatics/btx673.
Díaz-Del-Pino, S., Falgueras, J., Perez-Wohlfeil, E., & Trelles, O. (2018). mORCA: sailing bioinformatics world with mobile devices. Bioinformatics (Oxford, England), 34(5), 869-870. https://doi.org/10.1093/bioinformatics/btx673
Díaz-Del-Pino, Sergio, et al. "mORCA: sailing bioinformatics world with mobile devices." Bioinformatics (Oxford, England) vol. 34,5 (2018): 869-870. doi: https://doi.org/10.1093/bioinformatics/btx673
Díaz-Del-Pino S, Falgueras J, Perez-Wohlfeil E, Trelles O. mORCA: sailing bioinformatics world with mobile devices. Bioinformatics. 2018 Mar 01;34(5):869-870. doi: 10.1093/bioinformatics/btx673. PMID: 29069310; PMCID: PMC6031017.
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Computational approaches for systems metabolomics.

Current opinion in biotechnology

Krumsiek J, Bartel J, Theis FJ.
PMID: 27135552
Curr Opin Biotechnol. 2016 Jun;39:198-206. doi: 10.1016/j.copbio.2016.04.009. Epub 2016 Apr 30.

Systems genetics is defined as the simultaneous assessment and analysis of multi-omics datasets. In the past few years, metabolomics has been established as a robust tool describing an important functional layer in this approach. The metabolome of a biological...

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Krumsiek J, Bartel J, Theis FJ. Computational approaches for systems metabolomics. Curr Opin Biotechnol. 2016;39:198-206doi: 10.1016/j.copbio.2016.04.009.
Krumsiek, J., Bartel, J., & Theis, F. J. (2016). Computational approaches for systems metabolomics. Current opinion in biotechnology, 39198-206. https://doi.org/10.1016/j.copbio.2016.04.009
Krumsiek, Jan, et al. "Computational approaches for systems metabolomics." Current opinion in biotechnology vol. 39 (2016): 198-206. doi: https://doi.org/10.1016/j.copbio.2016.04.009
Krumsiek J, Bartel J, Theis FJ. Computational approaches for systems metabolomics. Curr Opin Biotechnol. 2016 Jun;39:198-206. doi: 10.1016/j.copbio.2016.04.009. Epub 2016 Apr 30. PMID: 27135552.
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Showing 1 to 12 of 689 entries