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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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OMICS studies: How about metadata checklist and data publications?.

Journal of proteome research

Kolker E, Stewart E.
PMID: 24494788
J Proteome Res. 2014 Mar 07;13(3):1783-4. doi: 10.1021/pr4011662. Epub 2014 Feb 05.

Data fully utilized by the community resources promote progress rather than repetition. Effective data sharing can accelerate the transition from data to actionable knowledge, yet barriers to data sharing remain, both technological and procedural. The DELSA community has tackled...

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Kolker E, Stewart E. OMICS studies: How about metadata checklist and data publications?. J Proteome Res. 2014;13(3):1783-4doi: 10.1021/pr4011662.
Kolker, E., & Stewart, E. (2014). OMICS studies: How about metadata checklist and data publications?. Journal of proteome research, 13(3), 1783-4. https://doi.org/10.1021/pr4011662
Kolker, Eugene, and Stewart, Elizabeth. "OMICS studies: How about metadata checklist and data publications?." Journal of proteome research vol. 13,3 (2014): 1783-4. doi: https://doi.org/10.1021/pr4011662
Kolker E, Stewart E. OMICS studies: How about metadata checklist and data publications?. J Proteome Res. 2014 Mar 07;13(3):1783-4. doi: 10.1021/pr4011662. Epub 2014 Feb 05. PMID: 24494788.
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BATMat: Bioinformatics Autodiscovery of Training Materials.

Briefings in bioinformatics

Lapatas V, Stefanidakis M.
PMID: 26330576
Brief Bioinform. 2016 Jul;17(4):728-30. doi: 10.1093/bib/bbv071. Epub 2015 Sep 01.

UNLABELLED: We present Bioinformatics Autodiscovery of Training Materials (BATMat), an open-source, Google-based, targeted, automatic search tool for training materials related to bioinformatics. BATMat helps gain access with one click to filtered and portable information containing links to existing materials...

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Lapatas V, Stefanidakis M. BATMat: Bioinformatics Autodiscovery of Training Materials. Brief Bioinform. 2015;17(4):728-30doi: 10.1093/bib/bbv071.
Lapatas, V., & Stefanidakis, M. (2016). BATMat: Bioinformatics Autodiscovery of Training Materials. Briefings in bioinformatics, 17(4), 728-30. https://doi.org/10.1093/bib/bbv071
Lapatas, Vasileios, and Stefanidakis, Michalis. "BATMat: Bioinformatics Autodiscovery of Training Materials." Briefings in bioinformatics vol. 17,4 (2016): 728-30. doi: https://doi.org/10.1093/bib/bbv071
Lapatas V, Stefanidakis M. BATMat: Bioinformatics Autodiscovery of Training Materials. Brief Bioinform. 2016 Jul;17(4):728-30. doi: 10.1093/bib/bbv071. Epub 2015 Sep 01. PMID: 26330576.
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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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Social network Analysis-based classifier (SNAc): A case study on time course gene expression data.

Computer methods and programs in biomedicine

Üçer S, Koçak Y, Ozyer T, Alhajj R.
PMID: 28859830
Comput Methods Programs Biomed. 2017 Oct;150:73-84. doi: 10.1016/j.cmpb.2017.06.015. Epub 2017 Jul 24.

BACKGROUND AND OBJECTIVES: Social Network Analysis is an attractive approach to model and analyze complex networks. In recent years, several bioinformatics related networks have been modeled and analyzed thoroughly using social network analysis. The objective of this study is...

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Üçer S, Koçak Y, Ozyer T, et al. Social network Analysis-based classifier (SNAc): A case study on time course gene expression data. Comput Methods Programs Biomed. 2017;150:73-84doi: 10.1016/j.cmpb.2017.06.015.
Üçer, S., Koçak, Y., Ozyer, T., & Alhajj, R. (2017). Social network Analysis-based classifier (SNAc): A case study on time course gene expression data. Computer methods and programs in biomedicine, 15073-84. https://doi.org/10.1016/j.cmpb.2017.06.015
Üçer, Serkan, et al. "Social network Analysis-based classifier (SNAc): A case study on time course gene expression data." Computer methods and programs in biomedicine vol. 150 (2017): 73-84. doi: https://doi.org/10.1016/j.cmpb.2017.06.015
Üçer S, Koçak Y, Ozyer T, Alhajj R. Social network Analysis-based classifier (SNAc): A case study on time course gene expression data. Comput Methods Programs Biomed. 2017 Oct;150:73-84. doi: 10.1016/j.cmpb.2017.06.015. Epub 2017 Jul 24. PMID: 28859830.
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NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity.

Computational biology and chemistry

Lu C, Wang J, Zhang Z, Yang P, Yu G.
PMID: 27670689
Comput Biol Chem. 2016 Dec;65:203-211. doi: 10.1016/j.compbiolchem.2016.09.005. Epub 2016 Sep 13.

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the...

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Lu C, Wang J, Zhang Z, et al. NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Comput Biol Chem. 2016;65:203-211doi: 10.1016/j.compbiolchem.2016.09.005.
Lu, C., Wang, J., Zhang, Z., Yang, P., & Yu, G. (2016). NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Computational biology and chemistry, 65203-211. https://doi.org/10.1016/j.compbiolchem.2016.09.005
Lu, Chang, et al. "NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity." Computational biology and chemistry vol. 65 (2016): 203-211. doi: https://doi.org/10.1016/j.compbiolchem.2016.09.005
Lu C, Wang J, Zhang Z, Yang P, Yu G. NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Comput Biol Chem. 2016 Dec;65:203-211. doi: 10.1016/j.compbiolchem.2016.09.005. Epub 2016 Sep 13. PMID: 27670689.
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Showing 1 to 12 of 1580 entries